Apparatus, system, and method for detecting physiological movement from audio and multimodal signals

ABSTRACT

Methods and devices provide physiological movement detection with active sound generation. In some versions, a processor may detect breathing and/or gross body motion. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user&#39;s vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the sound signal from the user. The processor may process the reflected sound, such as by a demodulation technique. The processor may detect breathing from the processed reflected sound signal. The sound signal may be produced as a series of tone pairs in a frame of slots or as a phase-continuous repeated waveform having changing frequencies (e.g., triangular or ramp sawtooth). Evaluation of detected movement information may determine sleep states or scoring, fatigue indications, subject recognition, chronic disease monitoring/prediction, and other output parameters.

1 CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/396,616, filed Sep. 19, 2016, the entire disclosureof which is hereby incorporated herein by reference.

2 BACKGROUND OF THE TECHNOLOGY 2.1 Field of the Technology

The present technology relates to detecting bio-motion associated withliving subjects. More particularly, the present technology relates tousing acoustic sensing to detect physiological movement such asbreathing movement, cardiac movement and/or other less cyclical bodymovement of a living subject.

2.2 Description of the Related Art

Monitoring the breathing and body (including limb) movement of a person,for example, during sleep, can be useful in many ways. For example, suchmonitoring could be useful in monitoring and/or diagnosing sleepdisordered breathing conditions, such as sleep apnea. Traditionally, thebarrier to entry for active radio location or ranging application isthat specialized hardware circuitry and antennas are required.

Smartphones and other portable electronic communication devices havebeen ubiquitous in daily life, even in developing countries wherelandlines are not available. It would be desirable to have a method formonitoring bio-motion (i.e., physiological movement) in an efficient,effective manner that does not require specialized equipment. Therealization of such a system and method would address a considerabletechnical challenge.

3 BRIEF SUMMARY OF THE TECHNOLOGY

The present technology concerns systems, methods, and apparatus fordetecting movement of a subject, for example, while the subject isasleep. Based on such movement detection, including for examplebreathing movement, the subject's movements, sleep relatedcharacteristics, sleep state and/or apnea events may be detected. Moreparticularly, a mobile application associated with a mobile device, suchas a smartphone, tablet, etc. uses the mobile device sensors, such as anintegrated, and/or externally connectable, speaker and microphone todetect breathing and motion.

Some versions of the present technology may include a processor-readablemedium, having stored thereon processor-executable instructions which,when executed by a processor, cause the processor to detectphysiological movement of a user. The processor-executable instructionsmay comprise instructions to control producing, via a speaker coupled toan electronic processing device, a sound signal in a vicinity thatincludes a user. The processor-executable instructions may compriseinstructions to control sensing, via a microphone coupled to theelectronic processing device, a sound signal reflected from the user.The processor-executable instructions may comprise instructions toprocess the sensed sound signal. The processor-executable instructionsmay comprise instructions to detect a breathing signal from theprocessed sound signal.

In some versions, the sound signal may be in an inaudible sound range.The sound signal may include a tone pair forming a pulse. The soundsignal may include a sequence of frames. Each of the frames of thesequence may comprise a series of tone pairs where each tone pair isassociated with a respective time slot within the frame. A tone pair mayinclude a first frequency and a second frequency. The first frequencyand second frequency may be different. The first frequency and secondfrequency may be orthogonal to each other. The series of tone pairs in aframe may comprise a first tone pair and a second tone pair, whereinfrequencies of a first tone pair may be different from frequencies of asecond tone pair. A tone pair of a time slot of the frame may have azero amplitude at a beginning and an end of the time slot, and may havea ramping amplitude to and from a peak amplitude between the beginningand the end.

In some versions, a time width of the frame may vary. The time width maybe a width of a slot of the frame. The time width may be a width of theframe. A sequence of tone pairs of a frame of slots may form a patternof different frequencies with respect to different slots of the frame.The pattern of different frequencies may repeat in a plurality offrames. The pattern of different frequencies may change for differentframes of slots in a plurality of frames of slots.

In some versions, the instructions to control producing the sound signalmay include a tone pair frame modulator. The instructions to controlsensing the sound signal reflected from the user may include a framebuffer. The instructions to process the sensed sound signal reflectedfrom the user may include a demodulator to produce one or more basebandmotion signals comprising the breathing signal. The demodulator mayproduce a plurality of baseband motion signals, where the plurality ofbaseband motion signals may include quadrature baseband motion signals.

In some versions, the processor-readable medium may includeprocessor-executable instructions to process the plurality of basebandmotion signals. The processor-executable instructions to process theplurality of baseband motion signals may include an intermediatefrequency processing module and an optimization processing module, toproduce a combined baseband motion signal from the plurality of basebandmotion signals. The combined baseband motion signal may include thebreathing signal. In some versions, the instructions to detect thebreathing signal may comprise determining a breathing rate from thecombined baseband motion signal. In some versions of the sound signal, aduration of a respective time slot of the frame may be equal to onedivided by a difference between frequencies of a tone pair.

In some versions of the present technology, the sound signal may includea repeated waveform with changing frequencies. The repeated waveform maybe phase-continuous. The repeated waveform with changing frequencies mayinclude one of a ramp sawtooth, triangular and a sinusoidal waveform.The processor-readable medium may further include processor-executableinstructions including instructions to vary one or more parameters of aform of the repeated waveform. The one or more parameters may includeany one or more of (a) a location of a peak in a repeated portion of therepeated waveform, (b) a slope of a ramp of a repeated portion of therepeated waveform, and (c) a frequency range of a repeated portion ofthe repeated waveform. In some versions, a repeated portion of therepeated waveform may be a linear function or a curve function thatchanges frequency of the repeated portion. In some versions, therepeated waveform with changing frequencies may include a symmetrictriangular waveform. In some versions, the instructions to controlproducing the sound signal include instruction to loop sound datarepresenting a waveform of the repeated waveform. The instructions tocontrol sensing the sound signal reflected from the user may includeinstructions to store sound data sampled from the microphone. Theinstructions to control processing the sensed sound signal may includeinstruction to correlate the produced sound signal with the sensed soundsignal to check synchronization.

In some versions, the instructions to process the sensed sound signalmay include a down converter to produce data comprising the breathingsignal. The down converter may mix a signal representing the producedsound signal with the sensed sound signal. The down converter may filteran output of the mixing of a signal representing the produced soundsignal and the sensed sound signal. The down converter may window afiltered output of the mixing of the signal representing the producedsound signal and the sensed sound signal. The down converter may producea frequency domain transformation matrix of a windowed filtered outputof the mixing of the signal representing the produced sound signal andthe sensed sound signal. The processor-executable instructions to detectthe breathing signal may extract amplitude and phase information frommultiple channels of a data matrix produced by the down converter. Theprocessor-executable instructions to detect the breathing signal mayfurther include processor-executable instructions to calculate aplurality of features from the data matrix. The plurality of featuresmay include any one of more of (a) an In-band squared over full bandmetric, (b) an in-band metric, (c) a Kurtosis metric, and (d) afrequency domain analysis metric. The processor-executable instructionsto detect the breathing signal may generate a breathing rate based onthe plurality of features.

In some versions, the processor-executable instructions may furtherinclude instructions to calibrate sound-based detection of body movementthat by an assessment of one or more characteristics of the electronicprocessing device. The processor-executable instructions may furtherinclude instructions to generate the sound signal based on theassessment. The instructions to calibrate sound-based detection maydetermine at least one hardware, environment, or user specificcharacteristic. The processor-executable instructions may furtherinclude instructions to operate a pet set up mode wherein a frequencyfor producing the sound signal may be selected based on user input andfor producing one or more test sound signals.

In some versions, the processor-executable instructions may furtherinclude instructions to discontinue producing the sound signal based ona detected user interaction with the electronic processing device. Thedetected user interaction may include any one or more of: detection ofmovement of the electronic processing device with an accelerometer,detection of pressing of a button, detection of touching of a screen,detection of an incoming phone call. The processor-executableinstructions may further include instructions to initiate producing thesound signal based on a detected absence of user interaction with theelectronic processing device.

In some versions, the processor-executable instructions may furtherinclude instructions to detect gross body movement based on processingof the sensed sound signal reflected from the user. In some versions,the processor-executable instructions may further include instructionsto process audio signals sensed via the microphone to evaluate any oneor more of environmental sounds, speech sounds and breathing sounds todetect user motion. In some versions, the processor-executableinstructions may further include instructions to process a breathingsignal to determine any one or more of (a) a sleep state indicatingsleep; (b) a sleep state indicating awake; (c) a sleep stage indicatingdeep sleep; (d) a sleep stage indicating light sleep; and (e) a sleepstage indicating REM sleep.

In some versions, the processor-executable instructions may furtherinclude instructions to operate a setup mode to detect sound frequenciesin a vicinity of the electronic processing device and select a frequencyrange for the sound signal that is different from the detected soundfrequencies. In some versions, the instructions to operate the setupmode may select a range of frequencies non-overlapping with the detectedsound frequencies.

In some versions of the present technology, a server may have access toany of the processor-readable mediums described herein. The server maybe configured to receive requests for downloading theprocessor-executable instructions of the processor-readable medium(s) toan electronic processing device over a network.

In some versions of the present technology, a mobile electronic deviceor electronic processing device may include one or more processors; aspeaker coupled to the one or more processors; a microphone coupled tothe one or more processors; and a processor-readable medium of any ofthe processor readable mediums describe herein.

Some versions of the present technology involve a method of a serverhaving access to any processor-readable medium described herein. Themethod may include receiving, at the server, a request for downloadingthe processor-executable instructions of the processor-readable mediumto an electronic processing device over a network. The method may alsoinclude transmitting the processor-executable instructions to theelectronic processing device in response to the request.

Some versions of the present technology involve a method of a processorfor detecting body movement using a mobile electronic device. The methodmay include accessing, with a processor, any of the processor-readablemediums described herein. The method may include executing, in theprocessor, any of the processor-executable instructions of the processorreadable medium(s).

Some versions of the present technology involve a method of a processorfor detecting body movement using a mobile electronic device. The methodmay include controlling producing, via a speaker coupled to the mobileelectronic device, a sound signal in a vicinity that includes a user.The method may include controlling sensing, via a microphone coupled tothe mobile electronic device, a sound signal reflected from the user.The method may include processing the sensed reflected sound signal. Themethod may include detecting a breathing signal from the processedreflected sound signal.

Some versions of the present technology involve a method for detectingmovement and breathing using a mobile electronic device. The method mayinclude transmitting, via a speaker on the mobile electronic device, asound signal towards a user. The method may include sensing, via amicrophone on the mobile electronic device, a reflected sound signal,the reflected sound signal being reflected from the user. The method mayinclude detecting a breathing and motion signal from the reflected soundsignal. The sound signal may be an inaudible or audible sound signal. Insome versions, prior to transmitting, the method may involve modulatingthe sound signal using one of a FMCW modulation scheme, a FHRGmodulation scheme, an AFHRG modulation scheme, a CW modulation scheme, aUWB modulation scheme, or an ACW modulation scheme. Optionally, thesound signal may be a modulated low frequency ultrasonic sound signal.The signal may include a plurality of frequency pairs transmitted as aframe. In some versions, upon sensing the reflected sound signal, themethod may include demodulating the reflected sound signal. Thedemodulating may include performing a filter operation on the reflectedsound signal; and synchronizing the filtered reflected sound signal withtiming of the transmitted sound signal. In some versions generating thesound signal may include performing a calibration function to assess oneor more characteristics of the mobile electronic device; and generatingthe sound signal based on the calibration function. The calibrationfunction may be configured to determine at least one hardware,environment, or user specific characteristic. The filter operation mayinclude a high pass filter operation.

Some versions of the present technology involve a method of detectingmotion and breathing. The method may include producing a sound signaldirected towards a user. The method may include sensing a sound signalreflected from the user. The method may include detecting a breathingand motion signal from the sensed reflected sound signal. In someversions the producing, transmitting, sensing, and detecting may beperformed at a bedside device. Optionally, the bedside device may be atherapy device such as CPAP device.

The methods, systems, devices and apparatus described herein can provideimproved functioning in a processor, such as of a processor of a generalor specific purpose computer, portable computer processing device (e.g.,mobile phone, tablet computer etc.), respiratory monitor and/or otherrespiratory apparatus utilizing a microphone and speaker. Moreover, thedescribed methods, systems, devices and apparatus can provideimprovements in the technological field of automated management,monitoring and/or prevention and/or evaluation of respiratory conditionand sleep condition, including, for example, sleep apnea.

Of course, portions of the aspects may form sub-aspects of the presenttechnology. Also, various ones of the sub-aspects and/or aspects may becombined in various manners and also constitute additional aspects orsub-aspects of the present technology.

Other features of the technology will be apparent from consideration ofthe information contained in the following detailed description,abstract, drawings and claims.

4 BRIEF DESCRIPTION OF THE DRAWINGS

The present technology is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings, in whichlike reference numerals refer to similar elements including:

FIG. 1 illustrates an example processing device for receiving audioinformation from a sleeper that may be suitable for implementation ofthe processes of the present technology;

FIG. 2 is a schematic illustration of a system in accordance with anexample of the present technology.

FIG. 2A illustrates a high level architectural block diagram of a systemfor implementing aspects of the present technology.

FIG. 3 is conceptual diagram of a mobile device configured in accordancewith some forms of the present technology.

FIG. 4 illustrates an example FHRG sonar frame.

FIG. 5 illustrates an example of an AFHRG transceiver frame.

FIGS. 6A, 6B and 6C illustrate an example of pulsed AFHRG frames.

FIG. 7 is a conceptual diagram of an (A)FHRG architecture.

FIG. 8 illustrates an example of an isotropic omnidirectional antennavs. a directional antenna.

FIG. 9 illustrates an example of a sinc filter response.

FIG. 10 illustrates an example of attenuation characteristics of a sincfilter.

FIG. 11 is a graph illustrating an example of a baseband breathingsignal.

FIG. 12 illustrates an example of initial synchronization with atraining tone frame.

FIG. 13 illustrates an example of an AFHRG frame with an intermediatefrequency.

FIG. 14 illustrates an example of an AToF frame.

FIGS. 15A, 15B and 15C illustrate signal characteristics of an exampleof an audible version of an FMCW ramp sequence.

FIGS. 16A, 16B and 16C illustrate signal characteristics of an exampleof an FMCW sinusoidal profile.

FIG. 17 illustrates signal characteristics of an example sound signal(e.g., inaudible sound) in the form of a triangular waveform.

FIGS. 18A and 18B illustrate demodulation of a detected breathingwaveform from the inaudible triangle emitted by a smart device'sloudspeaker of FIG. 17 with up ramp processing shown in FIG. 18A anddown ramp processing shown in FIG. 18B.

FIG. 19 illustrates an exemplar methodology of an FMCW processing flowoperator/module, including “2D” (two dimensional) signal processing.

FIG. 20 illustrates an exemplar methodology of a Down Conversionoperator/module, part of an FMCW processing flow of FIG. 19.

FIG. 21 illustrates an exemplar methodology of a 2D Analysisoperator/module, which may be part of an FMCW processing flow of FIG.19.

FIG. 22 illustrates a methodology for performing absence/presencedetection by an absence/presence operator/module.

FIG. 23A shows several signals on a graph illustrating sensingoperations at various detection ranges over time in a “2D” segment ofdata where a person moves out and into various sensing ranges of adevice 100

FIG. 23B is a portion of the signals graph of FIG. 23A showing area BBfrom FIG. 23A.

FIG. 23C is a portion of the signals graph of FIG. 23A showing area CCfrom FIG. 23A.

5 DETAILED DESCRIPTION OF EXAMPLES OF THE TECHNOLOGY

Before the present technology is described in further detail, it is tobe understood that the technology is not limited to the particularexamples described herein, which may vary. It is also to be understoodthat the terminology used in this disclosure is for the purpose ofdescribing particular examples discussed and is not intended to belimiting.

The following description is provided in relation to various forms ofthe present technology that may share common characteristics orfeatures. It is to be understood that one or more features of any oneexemplary form may be combinable with one or more features of anotherform. In addition, any single feature or combination of features in anyof form described herein may constitute a further exemplary form.

5.1 Screening, Monitoring, and Diagnosis

The present technology concerns systems, methods, and apparatus fordetecting movement of a subject, including, for example, breathingmovement and/or cardiac related chest movement, such as while thesubject is asleep. Based on such breathing and/or other movementdetection, the subject's sleep state and apnea events may be detected.More particularly, a mobile application associated with a mobile device,such as a smartphone, tablet, etc. uses the mobile device sensors, suchas a speaker and microphone to detect such motion.

An example system suitable for implementing the present technology isnow described with reference to FIGS. 1 to 3, as best shown in FIG. 2. Amobile device 100, or mobile electronic device, configured with anapplication 200 for detecting movement of subject 110, may be placed ona bedside table near subject 110. Mobile device 100 may be, for example,a smartphone or tablet having one or more processors. The processor(s)may be configured to, among other things, execute the functions ofapplication 200, including causing an audio signal to be generated andtransmitted, typically through the air as a generally open orunrestricted medium such as in a room vicinity of the device, receivinga reflection of the transmitted signal by sensing it with, for example,a transducer such as a microphone, and processing the sensed signal todetermine body movement and respiration parameters. Mobile device 100may comprise, among other components, a speaker and a microphone. Thespeaker may be used to transmit the generated audio signal and themicrophone to receive the reflected signal. Optionally, the sound-basedsensing methodologies of mobile device may be implemented in or by othertypes of devices such as a bedside device (e.g., a respiratory therapydevice such as a continuous positive airway pressure (e.g., “CPAP”)device or high flow therapy device). Examples of such devices, includinga pressure device or blower (e.g., a motor and impeller in a volute),one or more sensors and a central controller of the pressure device orblower, may be considered in reference to the devices described inInternational Patent Publication No. WO/2015/061848 (Appl. No.PCT/AU2014/050315) filed on Oct. 28, 2014, and International PatentPublication No. WO/2016/145483 (Appl. No. PCT/AU2016/050117) filed onMar. 14, 2016, the entire disclosures of which are incorporated hereinby reference.

FIG. 2A illustrates a high level architectural block diagram of a systemfor implementing aspects of the present technology. This system may beimplemented, for example, in mobile device 100. A signal generationcomponent 210 may be configured to generate an audio signal fortransmission towards a subject. As described herein, the signal may beaudible or inaudible. For example, as discussed in more detail herein,the sound signal may, in some versions, be generated in a low frequencyultrasonic ranges, such about seventeen (17) kilohertz (KHz) totwenty-three (23) KHz, or in a range of about eighteen (18) KHz totwenty-two (2) KHz. Such frequency ranges are generally consideredherein to be inaudible sound ranges. The generated signal may betransmitted via transmitter 212. In accordance with the disclosure, thetransmitter 212 may be a mobile phone speaker. Once the generated signalhas been transmitted, sound waves, including those waves reflected fromthe subject, may be sensed by receiver 214, which may be a mobile phonemicrophone.

As shown at 216, a signal recovery and analog-to-digital signalconversion stage may occur. The recovered signal may be demodulated toretrieve a signal representing a respiratory parameter, as shown at 218such as in a demodulation processing module. The audio components of thesignal may also be processed, as shown at 220, such as in an audioprocessing module. Such processing may include, for example, passiveaudio analysis to extract the sounds of breathing or other movement(including different types of activity such as rolling over in bed, PLM(periodic leg movement), RLS (restless leg syndrome) etc.) snoring,gasping, and wheezing. Audio component processing may also extract thesounds of interfering sources, such speech, TV, other media playback,and other ambient/environmental audio/noise sources. The output ofdemodulation phase at 218 may undergo both in-band processing at 222,such as by an in-band processing module, and out-of-band processing at224 such as by an out-of-band processing module. For example, in-bandprocessing at 222 may be directed to that portion of the signal bandcontaining the respiratory and cardiac signal. Out-band processing at224 may be directed to those portions of the signal containing othercomponents, such as gross or fine movement (e.g., rollovers, kicks,gestures, moving around a room, etc.) The outputs of the in-bandprocessing at 222 and out-band processing at 224 may then be provided toa signal post-processing at 230, such as in one or more signal postprocessing module(s). Signal post-processing at 230 may include, forexample, respiration/heart signal processing at 232 such as in arespiration/heart signal processing module, signal quality processing at234 such as in a signal quality processing module, gross body movementprocessing at 236 such as in a body movement processing module, andabsence/presence processing at 238, such as in an absence/presenceprocessing module.

While not shown in FIG. 2A, the outputs of signal post processing at 230may undergo a second post-processing stage that provides sleep state,sleep scoring, fatigue indication, subject recognition, chronic diseasemonitoring and/or prediction, sleep disordered breathing event detectionand other output parameters, such as from an evaluation of any of themotion characteristics of the produced motion signal(s). (e.g.,respiratory related movement (or absence thereof), cardiac relatedmovement, arousal-related movements, periodic leg movement, etc.). Inthe example of FIG. 2A, an optional sleep staging processing at 241,such as in a sleep staging processing module, is shown. However, any oneor more of such processing modules/blocks may optionally be added (e.g.,sleep scoring or staging, fatigue indication processing, subjectrecognition processing, chronic disease monitoring and/or predictionprocessing, sleep disordered breathing event detection processing, orother output processing, etc.). In some cases, the functions of signalpost-processing at 230 or the second post-processing stage may beperformed using any of the components, devices and/or methodologies ofthe apparatus, system and method described in any of the followingpatents or patent applications, wherein the entire disclosures of eachis incorporated by reference herein: International Patent ApplicationNo. PCT/US2007/070196, filed Jun. 1, 2007 and entitled “Apparatus,System, and Method for Monitoring Physiological Signs;” InternationalPatent Application No. PCT/US2007/083155, filed Oct. 31, 2007, entitled“System and Method for Monitoring Cardio-Respiratory Parameters;”International Patent Application No. PCT/US2009/058020, filed Sep. 23,2009, entitled “Contactless and Minimal-Contact Monitoring of Quality ofLife Parameters for Assessment and Intervention;” InternationalApplication No. PCT/US2010/023177, filed Feb. 4, 2010, entitled“Apparatus, System, and Method for Chronic Disease Monitoring;”International Patent Application No. PCT/AU2013/000564, filed Mar. 30,2013, entitled “Method and Apparatus for Monitoring Cardio-PulmonaryHealth;” International Patent Application No. PCT/AU2015/050273, filedMay 25, 2015, entitled “Methods and Apparatus for Monitoring ChronicDisease;” International Patent Application No. PCT/AU2014/059311, filedOct. 6, 2014, entitled “Fatigue Monitoring and Management System;”International Patent Application No. PCT/AU2013/060652, filed Sep. 19,2013, entitled “System and Method for Determining Sleep Stage;”International Patent Application No. PCT/EP2016/058789, filed Apr. 20,2016, entitled “Detection and Identification of a Human fromCharacteristic Signals;” International Patent Application No.PCT/EP2016/069496, filed 17 Aug. 2016, entitled “Screener for SleepDisordered Breathing;” International Patent Application No.PCT/EP2016/069413, filed Aug. 16, 2016, entitled “Digital Range GatedRadio Frequency Sensor;” International Patent Application No.PCT/EP2016/070169, filed Aug. 26, 2016, entitled “Systems and Methodsfor Monitoring and Management of Chronic Disease;” and U.S. patentapplication Ser. No. 15/079,339, filed Mar. 24, 2016, entitled“Detection of Periodic Breathing.” Thus, in some examples, theprocessing of detected movement, including for example, the breathingmovement, may serve as a basis for determining any one or more of (a) asleep state indicating sleep; (b) a sleep state indicating awake; (c) asleep stage indicating deep sleep; (d) a sleep stage indicating lightsleep; and (e) a sleep stage indicating REM sleep. In this regard, whilethe sound related sensing technologies of the present disclosure providefor different mechanisms/processes for motion sensing such as using aspeaker and microphone and processing of the sound signals, whencompared to radar or RF sensing technologies as described in theseincorporated references, once a breathing signal, such as breathing rateis obtained with the sound sensing/processing methodologies described inthis specification) the principles of processing breathing or othermotion signal for an extraction of sleep states/stages information maybe implemented by the determination methodologies of these incorporatedreferences.

5.1.1 Mobile Device 100

Mobile device 100 may be adapted to provide an efficient and effectivemethod of monitoring a subject's breathing and/or other movement relatedcharacteristics. When used during sleep, the mobile device 100 and itsassociated methods can be used to detect the user's breathing andidentify sleep stages, sleep states, transitions between states,sleep-disordered breathing and/or other respiratory conditions. Whenused during wake, the mobile device 100 and its associated methods canbe used to detect movement such as of a subject breathing (inspiration,expiration, pause, and derived rate) and/or ballistocardiogram waveformand subsequent derived heart rate. Such parameters may be used forcontrolling a game (whereby a user is guided to reduce their respirationrate for relaxation purposes), or evaluating respiratory condition suchas of a subject with a chronic disease such as COPD, asthma, congestiveheart failure (CHF) etc., where the subject's baseline respiratoryparameter(s) change in the time before an exacerbation/decompensationevent occurs. The respiratory waveform may also be processed to detecttemporary cessation of breathing (such as central apnea, or the smallchest movements against an obstructive airway seen during an obstructiveapnea) or reduction in breathing (shallow breathing and/or reduction inbreathing rate, such as related to a hypopnea).

Mobile device 100 may include integrated chips, a memory and/or othercontrol instruction, data or information storage medium. For example,programmed instructions encompassing the assessment/signal processingmethodologies described herein may be coded on integrated chips in thememory of the device or apparatus to form an application specificintegrated chip (ASIC). Such instructions may also or alternatively beloaded as software or firmware using an appropriate data storage medium.Optionally, such processing instructions may be downloaded such as froma server over a network (e.g. an internet) to the mobile device suchthat when the instructions are executed, the processing device serves asa screening or monitoring device.

Accordingly, mobile device 100 may include a number of components asillustrated by FIG. 3. The mobile device 100 may include, among othercomponents, a microphone or sound sensor 302, a processor 304, a displayinterface 306, a user control/input interface 308, a speaker 310, and amemory/data storage 312, such as with the processing instructions of theprocessing methodologies/modules described herein.

One or more of the components of mobile device 100 may be integral withor operably coupled with mobile device 100. For example, microphone orsound sensor 302 may be integral with mobile device 100 or coupled withmobile device 100 such as through a wired or wireless link (e.g.,Bluetooth, Wi-Fi etc.).

Memory/data storage 312 may comprise a plurality of processor controlinstructions for controlling processors 304. For example, memory/datastorage 312 may comprise processor control instructions for causingapplication 200 to be performed by the processing instructions of theprocessing methodologies/modules described herein.

5.1.2 Motion and Breathing Detection Process

Examples of the present technology may be configured to use one or morealgorithms or processes, which may be embodied by application 200, todetect motion, breathing, and optionally sleep characteristics while auser is asleep using the mobile device 100. For example, application 200may be characterized by several sub-processes or modules. As shown inFIG. 2, application 200 may include an audio signal generation andtransmission sub-process 202, a motion and bio-physical characteristicdetection sub-process 204, a sleep quality characterization sub-process206, and a results output sub-process 208.

5.1.2.1 Generating and Transmitting an Audio Signal

According to some aspects of the present technology, an audio signal maybe generated and transmitted towards a user such as using one or moretones described herein. A tone provides pressure variation in a medium(e.g., air) at a particular frequency. For purposes of this description,the generated tones (or audio signals or sound signals) may be referredto as “sound”, “acoustic” or “audio” because they may be generated in alike manner to audible pressure waves (e.g., by a speaker). However,such pressure variations and tone(s) should be understood herein to beeither audible or inaudible, notwithstanding their characterization byany of the terms “sound”, “acoustic” or “audio.” Thus, the audio signalgenerated may be audible or inaudible, wherein the frequency thresholdof audibility across the human population varies by age. The typical“audio frequency” standard range is around 20 Hz to 20,000 Hz (20 kHz).The threshold of higher frequency hearing tends to reduce with age, withmiddle aged people often unable to hear sounds with frequencies above15-17 kHz, whereas a teenager may be able to hear 18 kHz. The mostimportant frequencies for speech are approximately in the range250-6,000 Hz. Speaker and microphone signal responses for typicalconsumer smartphones are designed to roll off above 19-20 kHz in manycases, with some extending to above 23 kHz and higher (especially wherethe device supports a sampling rate of greater than 48 kHz such as 96kHz). Therefore, for most people, it is possible to use signals in therange of 17/18 to 24 kHz and remain inaudible. For younger people thatcan hear 18 kHz but not 19 kHz, a band of 19 kHz to say 21 kHz could beemployed. It is noted that some household pets may be able to hearhigher frequencies (e.g., dogs up to 60 kHz and cats up to 79 kHz).

The audio signal may comprise, for example, sinusoidal waveforms,sawtooth chirps, triangular chirps, etc. As background, the term“chirp”, as used herein, is a short-term non-stationary signal thatcould, for example, have a sawtooth or triangular shape, with a linearor non-linear profile. Several types of signal processing methods may beused to produce and sense the audio signal including, for example,continuous wave (CW) homodyning, pulsed CW homodyning, frequencymodulated CW (FMCW), frequency hopping range gating (FHRG), adaptiveFHRG (AFHRG), ultra wideband (UWB), orthogonal frequency divisionmultiplexing (OFDM), adaptive CW, frequency shift keying (FSK), phaseshift keying (PSK), binary phase shift keying (BSPK), quadrature phaseshift keying (QPSK), and the generalized QPSK called quadratureamplitude modulation (QAM) etc.

According to some aspects of the present technology, a calibrationfunction, or calibration module, may be provided for assessing thecharacteristics of the mobile device. If a calibration function suggeststhat the hardware, environment, or user set up requires an audiblefrequency, coding may be overlaid on a spread spectrum signal that isacceptable to the user while still allowing active detection of anybiomotion signals in the sensing area. For example, it is possible to“hide” an audible or inaudible sensing signal in an audible sound suchas a music, TV, streaming source or other signal (e.g., a pleasing,repetitive sound that may optionally be synchronized to the detectedbreathing signal such as waves crashing on the shore that might be usedto aid sleep). This is similar to audio steganography, where messagesthat must be perceptually indiscernible are hidden in the audio signal,using techniques such as phase coding where elements of the phase areadjusted to represent the encoded data.

5.1.2.2 Motion and Bio-Physical Signal Processing

5.1.2.2.1 Technical Challenges

To achieve good motion signal to environmental related noise ratios,several room acoustic issues should be considered. Such issues mayinclude, for example, reverberation, bed clothes usage, and/or otherroom acoustic issues. In addition, specific monitoring devicecharacteristics, such as directionality of the mobile device or othermonitoring device, should also be considered.

5.1.2.2.1.1 Reverberation

A reverberation may be created when sound energy is confined to a spacewith reflective walls, such as a room. Typically, when a sound is firstgenerated, the listener first hears the direct sound from the sourceitself. Later, the user may hear discrete echoes caused by soundbouncing off room walls, the ceiling, and the floor. Over time,individual reflections may become indistinguishable and the listenerhears continuous reverberation that decays over time.

Wall reflections typically absorb very little energy at the frequenciesof interest. Accordingly, sound is attenuated by the air, and not by thewalls. Typical attenuation may be <1%. It takes approximately 400 ms ina typical room at audio frequencies for a sound attenuation of 60 dB tobe reached. Attenuation increases with frequency. For example, at 18kHz, the typical room reverberation time is reduced to 250 ms.

Several side effects are associated with reverberation. For example,reverberation results in room modes. Because of the reverberation energystorage mechanism, the input of acoustic energy to the room causesstanding waves at resonant or preferred modal frequencies (nλ=L). For anideal three-dimensional room, of dimensions Lx, Ly and Lz, the mainmodes are given by

$f_{p,q,r} = {\frac{c_{0}}{2}\sqrt{( \frac{p}{L_{x}} )^{2} + ( \frac{q}{L_{y}} )^{2} + ( \frac{r}{L_{z}} )^{2}}}$

These standing waves result in the loudness of the particular resonantfrequency being different at different locations of the room. This mayresult in signal level variation and associated fading at the sensingcomponent (e.g., sound sensor) that receives the sound signal. This isanalogous to multipath interference/fading of electromagnetic waves(e.g., RF) such as Wi-Fi; however, room reverberation and associatedartifacts such as fading are more severe for audio.

Room modes may present design issues for sonar systems. Such issues mayinclude, for example, fading, 1/f noise, and gross motion signals. Agross motion usually refers to a large physiological movement such as arollover in bed (or a user getting in or out of bed, which is associatedwith an absence before or after the event respectively). In onerealization, a movement can be considered as a binary vector (movementor no movement), with an associated activity index referring to theduration and intensity of movement (e.g., PLM, RLS or bruxism is anexample of a movement activity that is not a gross motion, as it relatesto limb(s) or grinding of the jaw, and not movement the full body whenchanging position in bed). The signal received by sound sensor 302(e.g., microphone) may experience fading of the signal strength and/orfading of the reflected signal from the user. This fading may be causedby standing waves due to reverberation and may result in a signalamplitude variation issue. 1/f noise (a signal with a power spectraldensity that is inversely proportional to the frequency of the signal)at breathing frequencies may occur due to room air currents disturbingthe room modes and resulting in a noise floor increase and hence asignal to noise reduction at breathing frequencies. Gross motion signalsfrom any movement in a room are due to the disturbance of the room modeenergy and may produce a resultant sensed/received signal strength andphase variation. However, while this is an issue, it can be seen that bydeliberately setting up room modes, a useful gross (large) motion andmore subtle activity detection can be performed, and indeed respirationanalysis for the case of one dominant motion source in the room (e.g., asingle person in a bedroom).

The term SONAR is used herein to include sound signals, acoustic, sonic,ultrasonic, and low frequency ultrasonic, for ranging and movementdetection. Such signals could range from DC to 50 kHz or above. Some ofthe processing techniques such as the FMCW physiological signalextraction may also be applicable to actual short range (e.g., up toapproximately 3 meters—such as for monitoring a living space or bedroom)RF (electromagnetic) radar sensors, such as those operating at 5.8 GHz,10.5 GHz, 24 GHz etc.

5.1.2.2.1.2 Bed Clothes

The use of bed clothes (e.g., duvets, comforters, blankets, etc.) mayseverely attenuate sound in a room. In some aspects, a duvet mayattenuate a sound signal twice at it travels and returns from a sleepingperson. A duvet surface also reflects the sound signal. If the breathingis seen at the duvet surface, then a duvet reflection signal can be usedfor monitoring breathing.

5.1.2.2.1.3 Phone Characteristics

The placement of the speaker and microphone on a smartphone are notalways optimal for acoustic sensing in a sonar-like application. Thetypical smartphone speaker to microphone has poor directionality.Generally, smartphone audio is designed for human speech, and notspecifically designed for sonar-like acoustic sensing applications.Moreover, placement of speakers and microphones vary among smartphonemodels. For example, some smartphones have the speaker at the back ofthe phone and the microphone on the side of the phone. As a result, themicrophone cannot easily “see” the speaker signal without first beingredirected by reflections. Additionally, the directionality of bothspeaker and microphone increase with frequency.

Room modes may enhance the omnidirectional nature of a smartphonemicrophone and speaker. Reverberation and associated room modes producestanding waves nodes throughout the room at the particular frequencies.Any movement in the room can be seen at these nodes as the movementdisturbs all room mode nodes. A smartphone microphone, when located at anode or antinode, acquires an omnidirectional characteristic due to thesound path being omnidirectional even though the microphone and speakerare directional.

5.1.2.2.2 Overcoming the Technical Challenges

According to aspects of the disclosure, specific modulation anddemodulation techniques may be applied to reduce or remove the impact ofthe identified and other technical challenges.

5.1.2.2.2.1 Frequency Hopping Range Gating (FHRG) and Adaptive FrequencyHopping Range Gating (AFHRG)

A frequency hopping range gated signal is a modulation demodulationtechnique that utilizes a sequence of discrete tones/frequencies whichoccupy a specified frequency range. Each tone, tone pair, or multi tonepulse in the tone sequence is transmitted for a particular duration,defined by the range requirement. A change to the particular duration ofsuch a tone pulse or tone pair results in a change of the detectionrange. A sequence of tone pairs or tones produces a tone frame or aframe of slots. Each slot may be considered a tone slot. Thus, a framemay include a plurality of slots, where each slot may contain a tone ortone pair. Typically, to promote improved inaudibility, each tone pairmay have a time duration that is equal to the time duration of a slot ofthe frame. However, this is not required. In some cases, the slots of aframe may be of equal or unequal width (time duration). Thus, the tonepairs of a frame may be of equal or unequal duration within the frame.The sound modulation can contain guard bands (quiet periods) betweentones, and then between frames, in order to allow better separation ofsensed/received tones such that range gating can be achieved. FHRG canbe used in audio frequencies that are usable. FHRG can apply frequencyhopping with frequency dithering, and/or timing dithering. Timingdithering means that frame and/or internal slot timing can vary (e.g.,time period of the slot varies, or the start time of the frame or thestart time of the slot in a time series may vary), such as to reduce therisk of room modes building up, and also to reduce the risk ofinterference between other SONAR sensing in the environment. Forexample, in the case of a four-slot frame, while frame width may remainconstant, time dithering can permit at least one smaller slot width(e.g. a tone pair of shorter duration) and at least one larger slotwidth (e.g., a tone pair of longer duration) in the frame while theother slot widths may be unchanged). This can provide a slight variationin the particular range detected by each slot of the frame. Frequencydithering can allow for multiple “sensors” (e.g., two or more sensingsystems in a room) to coexist in a common vicinity. For example, byemploying frequency dithering, the frequency of a slot (tone or tonepair) is moving such that there is a statistically insignificant chancethat interference occurs between the “sensors” in normal operation. Bothtime and frequency dithering can reduce the risk of interference fromnon-SONAR sources in the room/environment. Synchronization demodulationrequires precise understanding of alignment between sequences, e.g., byusing special training sequences/pulses and then patterndetection/matched filters to align the frames and recover the actualoffset.

FIG. 4 illustrates an example FHRG sonar frame. As shown, 8 individualpulsed sonar (analogous to a radar system) signals may be transmitted ineach frame. Each pulse may contain 2 orthogonal pulse frequencies, andeach pulse may be 16 ms long. As a result, 16 separate frequencies maybe transmitted in each 128 ms transceiver frame. The block (frame) thenrepeats. It may exploit orthogonal frequencies of OFDM to optimize thefrequency use in a confined bandwidth and enhance signal-to-noise. Theorthogonal Dirac Comb frequencies also allow for frame timing ditheringand tone frequency dithering to assist noise reduction. The tone pairshapes the resulting pulse, and this shaping aids inaudibility.

An adaptive frequency hopping range gating (AFHRG) system keeps changingfrequency of the tone frequency sequence over time, using tones of adefined length (e.g., 16 or 8 ms) before switching to another tone (fora total of four tones). Thus, a block of tones is generated, and theblock (frame) then repeats at 31.25 Hz. For an AFHRG system, the tonepatterns shift on each frame. These patterns can shift constantly. Thus,a frame can have a pattern of frequencies that differs from a pattern offrequencies of other frames of a series of frames. Thus, the pattern ofdifferent frequencies may change. A frame can also have differentfrequencies within the frame, such as within a slot, or differences withrespect to different slots. In addition, each frame may adapt, byadjusting its frequency inside a fixed band, to mitigate fading. FIG. 5illustrates an example of a single 32 ms transceiver frame for an AFHRGsystem. As shown in FIG. 5, four individual homodyne pulsed sonarsignals may be transmitted in each frame. Each signal has an 8 ms timeof flight. At 8 ms time of flight, the range, including both directions,is 2.7 meters giving an effective actual frame of 1.35 m, and a pulserepetition frequency of 31.25 Hz. Additionally, each pulse may includetwo pulse frequencies (e.g., essentially simultaneous tones), as shownin FIG. 5, resulting in 8 separate pulsed homodyne signals in a single32 ms transceiver frame.

AFHRG may be configured to use Dirac Comb frequencies and orthogonalpulse pairs to optimize available bandwidth. Each “pair” can be in aslot within a frame. Thus, multiple frames can contain many “pairs” inrepeating or non-repeating patterns. Separate frequency pairs may beused for each timeslot, and may use linear or Costas Code frequencyhopping. The timeslot may be determined based on the desired rangedetection. For example, as shown in FIG. 5, t_(ts)=8 ms for a 1.3 mdetection range. Frequency pairs may be generated as: {A Sin(ω₁t)−ASin(ω₂t)} with

${\omega_{1} - \omega_{2}} = {\frac{2\pi}{tts}.}$

Each frame may adapt, by adjusting its frequency inside a fixed band, tomitigate fading once the requirement of the frequency pairs equation ismaintained. Thus, the adaption acts to maximize the available SNR(signal to noise ratio) from any subject(s) present in the detectionrange.

Each frequency pair may be chosen to optimize a desired bandwidth (e.g.,the 1 kHz bandwidth (18 kHz to 19 kHz)), to provide maximum isolationbetween frequencies, to provide maximum isolation between pulses, and/orto minimize inter pulse transients. These optimizations may be achievedfor n frequencies, each with a frequency separation df and a slot widthof tts such that:

${df} = {{f_{n} - f_{n - 1}} = {{{{BW}/n}\mspace{14mu}{and}\mspace{14mu}{df}} = \frac{1}{tts}}}$

For an 8 ms time slot duration and a 1 kHz bandwidth (say f_(n)=18,125Hz and f_(n-1)=18,000 Hz), the frequency separation df becomes:

df = 125  Hz = 1  kHz/8  and ${df} = \frac{1}{8\mspace{14mu}{ms}}$

Zero crossing is achieved by utilizing the trigonometric identity: sina−sin b=2 sin ½ (a−b) cos ½ (a+b), such that each timeslot comprises asinusoidal pulse amplitude with a frequency of f_(n)−½ df. Thus, theduration of a slot, or each slot, of a frame may be equal to one dividedby the difference between the frequencies of the tone pairs. Thus, in aframe, a time slot duration may be inversely proportional to thefrequency difference of the frequencies of the tones of a tone pair ofthe time slot.

FIGS. 6A, 6B and 6C illustrate an example of 5×32 ms pulsed frames foran example AFHRG system. Graph 602 in FIG. 6A shows 5×32 ms pulse frameson an x-axis of time and a y-axis of amplitude. Graph 602 is a timedomain representation of five 32 ms frames, of which each frame containsfour (4) slots—for a total of 5×4=20 tone pairs. A tone pair is in eachof slots 606-S1, 606-S2, 606-S3, 606-S4. The envelope of this isvarying, which exemplifies a “real world” speaker and mic combinationthat may be slightly less sensitive at higher frequencies. The slotsthat form a frame shown in FIG. 6A may be considered in more detail inthe graph 604 of FIG. 6B. Graph 604 shows a frequency domainrepresentation of a single 32 ms frame from FIG. 6A. It shows four slots606-S1, 606-S2, 606-S3, 606-S4 where each slot contains two tones 608T1,608T2 (a tone pair). Graph 604 has an x-axis of frequency, y-axis thatis arbitrary. In an example, the tones of the tone pairs of the frameare each different sound tones (e.g., different frequencies) in a rangeof 18000 Hz to 18875 Hz. Other frequency ranges, such as for inaudiblesound, may be implemented as discussed in more detail herein. Each tonepair of the tone pairs of the frame are generated sequentially (inseries) in the frame. The tones of a tone pair are producedsubstantially simultaneously in a common slot of the frame. The tonepair of a slot of FIGS. 6A and 6B may be considered in reference to thegraph 610 in FIG. 6C. Graph 610 is an illustration of a time domain viewof a single tone pair (e.g., tones 608T1, 608T2) in a frame showingtheir concurrent generation in a slot of the frame. Graph 610 has anx-axis of time, y-axis of amplitude. In the example, the amplitude ofthe tones are ramped up and down within the time period of the timeslot. In the example of the graph of 610, the tone amplitude ramp beginswith the slot start time at zero amplitude and ramps down to end withthe slot at zero amplitude. Such contemporaneous zero amplitude tonearrival at the beginning and end of the slot can improve inaudibilitybetween tone pairs of adjacent slot(s) that have the same end andbeginning slot amplitude characteristics.

Aspects of an FHRG or an AFHRG (herein both being referred to as“(A)FHRG”) architecture for processing audio signals will now bedescribed in reference to the methodologies of the processing modulesshown in FIG. 7. As shown in FIG. 7, reflected signals may be sensed,filtered via a module of high pass filter 702, and input to Rx framebuffer 704. The buffered Rx frames may be processed by IQ (in-phase andquadrature) demodulator 706. According to some aspects of thedisclosure, each of n (where n may be, for example, 4, 8 or 16)frequencies may be demodulated to baseband as I and Q components, wherethe baseband represents motion information (breathing/bodily movement,etc.) corresponding to changes in sensed distance that is detected withthe audio reflections (e.g., transmitted/produced and received/sensedaudio signals or tone pairs). An intermediate frequency (IF) stage 708is a processing module that outputs a single I and Q component frommultiple signals, which undergoes optimization in a module “IQOptimization” at 710 to produce a single combined output.

The multi IQ input information at 710 can be optimized and condensed toa single IQ baseband signal output for the algorithm input stage. Thesingle IQ output (effectively a combined signal from I and Q components)can be derived based on a selection of a candidate IQ pair based on theone with the highest signal quality, such as based on the clearestbreathing rate. For example, breathing rates may be detected from thebaseband signal (a candidate signal or combined signal) as described inInternational Application WO2015006364, the entire disclosure of whichis incorporated herein by reference. The single IQ output can also be anaverage of the input signals, or indeed an average or median of thederived breathings rates. Thus, such a module at 710 may include asumming and/or averaging process.

An AFHRG architecture allows for the optional addition of an IF stage.There are two possible IF stage methodologies—(i) comparing early, forexample, 4 ms (0.5 m range) with later, for example, 4 ms (0.5 m range)signal, and (ii) comparing the phase of a first tone with phase of asecond tone in a tone pair. Because the stage compares the level orphase change in the sensed sound signal within the time of flight (ToF)time, it can act to remove common mode signal artefacts such as motionand 1/f noise issues. A phase foldover recovery module 712 receives theI and Q component output of optimization stage at 710 and, afterprocessing, outputs an IQ baseband output. It can be desirable tominimize foldover in the demodulated signal, as this significantlycomplicates respiratory “in band” detection. Foldovers can be minimizedusing I/Q combination techniques, such as arctangent demodulation, usingreal-time center tracking estimation, or more standard dimensionalityreduction methods, such as principal components analysis. Foldoveroccurs if movement of the chest spans over a half wavelength ofapproximately ˜9 mm (such as for a CW frequency of 18 kHz at atemperature of 20 deg C., the speed of sound is approx. 343 m/s and thewavelength is approx. 19 mm). A dynamic center frequency strategy can beused to automatically correct for foldover—e.g., to reduce the severityor probability of occurrence of this behavior. In this case, iffrequency doubling, or an unusually jagged respiratory pattern(morphology) is detected, the system can move the center frequency to anew frequency to push the I/Q channels back into balance and out of afoldover situation. Reprocessing is typically required whenever theperson moves. The change in audio frequency is designed so as not to beaudible (unless a masking tone is in use). If movement is at lambda/4,we may see it on one channel. If movement is greater than lambda/2, thenwe are very likely to see it on both channels.

In an FHRG implementation, the frames/tone pair frame modulator at 714are controlled by the processor (with implicit multi frequency operationusing frames etc.). In an adaptive implementation such as AFHRG or AToF,the system further includes modules for fading detection and explicitfrequency shift operations not present in an FHRG implementation. Themodule of fading detector 718 provides a feedback mechanism to adjustthe parameters of the system (including frequency shift, modulationtype, frame parameters such as number of tones, spacing in time andfrequency etc.) to optimally detect motion including respiration, invarying channel conditions. Fading can be directly detected from theamplitude modulation (extracted via envelope detection) variation in thesensed sound signal, and/or via changes in specific tone pairs. Thefading detector may also receive secondary information from subsequentbaseband signal processing, although this may add some processing delay;this can be used to relate actual extracted breathing signalquality/morphology to the current channel conditions, to provide betteradaption to maximize useful signal. The fading detector may also processI/Q pairs (pre baseband respiration/heart rate analysis). By using aconfiguration of non-faded Tx (transmit) waveform(s), the finite emittedsignal power of the speaker can also be optimized/best utilized tomaximize the useful information received by the sound sensor/receiver Rx(receive), and demodulated/further processed to baseband. In some cases,the system may select a slightly suboptimal (in terms of short term SNR)set of frequencies for Tx, if the system is found to be more stable overa longer period of time (e.g., to avoid multipath variation in fading ona timescale similar to a respiration rate, that might cause“noise”/artefact in the desired demodulated respiration rate band).

Of course, if one or more individual tones are used in place of tonepairs, a similar architecture can be utilized to realize an adaptive CW(ACW) system.

5.1.2.2.2.2 FHRG Modulation Module and Demodulation Module Details5.1.2.2.2.2.1 FHRG Sonar Equation

The transmitted sound pressure signal generated by the smartphonespeaker is reflected by a target, and returns to be sensed at thesmartphone microphone. FIG. 8 illustrates an example of an isotropicomnidirectional antenna 802 vs. a directional antenna 804. For anomnidirectional source the sound pressure (P(x)) level will decreasewith distance x as:

${P(x)} = \frac{P_{0}}{4\pi x^{2}}$

The smartphone speaker is directional at 18 kHz hence:

${P(x)} = \frac{P_{0}}{4\pi x^{\gamma}}$

where γ is the speaker gain and has a value between 0 and 2, typically>1.

The target (e.g., a user in bed) has a specific cross section andreflection coefficient. Reflection from the target is also directional:

${P(x)} = {{\sigma( {1 - \alpha} )}\frac{P_{0}}{4\pi\; x^{\beta}}}$

where α is the reflector attenuation and has a value between 0 and 1,typically <0.1β is the reflector gain and has a value between 0 and 2, typically >1σ is the sonar cross-section.

As result, a transmitted signal of sound pressure P₀ will, on reflectionat a distance d, return to the smartphone with an attenuated level of:

${P_{rxs}(d)} = {{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}}$

The smartphone microphone will then see a portion of the reflected soundpressure signal. The percentage will be dependent on the microphoneeffective area A_(e):

${P_{rxm}(d)} = {A_{e}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}}$

In this way a small fraction of the transmit sound pressure signal isreflected by a target and returns to be sensed at the smartphonemicrophone.

5.1.2.2.2.2.2 FHRG Movement Signal(s)

A person within range of an FHRG system at a distance d from thetransceiver will reflect the active sonar transmit signal and produce areceive signal.

The sound (pressure wave) generated by the smartphone due to the modemsignal for any frequency f_(nm) is:

${P_{rx}( {x,t,w_{nm}} )} = {\frac{P_{0}}{x^{\gamma}}{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{14mu} t} + \frac{x}{\lambda_{nm}}} )}}$

The signal arriving at the target, at a distance d, for any individualfrequency is given as:

${P_{rx}( {x,t,w_{nm}} )} = {\frac{P_{0}}{x^{\gamma}}{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{14mu} t} + \frac{d}{\lambda_{nm}}} )}}$

The reflected signal arriving back at the smartphone microphone is:

${P_{rx}( {x,t,w_{nm}} )} = {{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{14mu} t} + \frac{2d}{\lambda_{nm}}} )}}$

If the target distance is moving with a sinusoidal variation about dsuch that

d(x,t,w _(b) ,A _(b))=d ₀ +A _(b) Sin(2πf _(b) t+θ)

where:

Breathing Frequency: w_(b)=2πf_(b)

Breathing Amplitude: A_(b)

Breathing Phase: θ

Nominal target distance: d₀

Because maximum breathing displacement A_(b) is small compared to targetdistance d, its effect on receive signal amplitude can be ignored. Asresult the smartphone microphone breathing signal becomes:

${P_{rx}( {x,t,w_{nm}} )} = {{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{14mu} t} + \frac{{2d_{0}} + {2A_{b}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{nm}}} )}}$

Because an idealized respiratory movement signal, which may be similarto a cardiac movement signal albeit at different frequencies anddisplacements, could be thought of as a sinusoidal function of asinusoidal function, it will have areas of maximum and minimumsensitivity for the same displacement peak to peak amplitude.

To correctly recover this signal, it is beneficial to utilize aquadrature phase receiver or similar so that sensitivity nulls aremitigated.

The I and Q baseband signals can then be utilized as a phasor I+jQ to:

-   -   1. Recover the RMS and Phase of the breathing signal    -   2. Recover the direction (phasor direction) of the movement    -   3. Recover foldover information by detecting when the phasor        changes direction

5.1.2.2.2.2.3 FHRG Demodulator Mixer (Module at 705 and 706 in FIG. 7)

The FHRG softmodem (frontend for acoustic sensing (sonar)detection—software based modulator/demodulator) transmits a modulatedsound signal through the speaker and senses the echo reflected signalfrom the target through the microphone. An example of a softmodemarchitecture is presented in FIG. 7. The softmodem receiver thatprocesses the sensed reflection as a received signal is designed toperform a multiplicity of tasks including:

-   -   High pass filter the received signal at audio frequencies    -   Synchronize with the frame timing of the modulated audio    -   Demodulate the signal to baseband utilizing a quadrature phase        synchronous demodulator    -   Demodulate the IF (intermediate frequency) component if present    -   Low pass filter (e.g., Sinc filter) the resultant in phase and        quadrature baseband signal    -   Reproduce the I and Q baseband signal from a multiplicity of I,Q        signals at different frequencies.

The softmodem demodulator (e.g., module at 705 and 706 in FIG. 7)utilizes a local oscillator (shown as “A Sin(w₁t)” in the module at 706)with a frequency synchronous to that of the received signal to recoverthe phase information. The module at 705 (the Frame Demodulator) selectsand separates each tone pair for subsequent IQ demodulation of eachpair—i.e., defining each (wt) for the IQ demodulation of a tone pair.The in-phase (I) signal recovery will be discussed. The quadraturecomponent (Q) is identical but utilizes a local oscillator (shown as “ACos(w₁t)” in the module at 706) with a 90-degree phase change.

Given a pressure signal sensed at the microphone that is accuratelyreproduced as an equivalent digital signal and the input high passfilter (e.g., filter 702) is ideal, the audio signal received by thesmartphone softmodem demodulator is:

${y\mspace{14mu}( {x,t,w_{nm}} )} = {{A\;{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{11mu} t} + \frac{2d}{\lambda_{nm}}} )}} + {\Sigma\mspace{14mu} D_{j}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{j}\mspace{14mu} t} + \varphi_{j}} )}}$

Where:

-   The amplitude A is determined by the sonar parameters:

$A = {{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}}$

-   The distance d is modulated by the target movement: d=d₀+A_(b)    Sin(2πf_(b) t+θ)-   Where static clutter reflections/interference signals are    represented as: ΣD Sin(2πf_(j)t+φ_(j))

The in-phase demodulator output signal for any received signal, whencorrectly synchronized, is:

$( {x,t,w_{nm}} ) = {{{Sin}( {w_{nm}\mspace{14mu} t} )}\{ {{A\;{Sin}\mspace{14mu} 2{\pi( {{f_{nm}\mspace{14mu} t} + \frac{2d}{\lambda_{nm}}} )}} + {\Sigma\mspace{14mu} D_{j}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{j}\mspace{14mu} t} + \varphi_{j}} )}} \}}$

This demodulation operation follows the trigonometric identity:

A Sin(w ₁ t+ω)B Sin(w ₂ t+θ)=AB Cos [(w ₁ t+ω)−(w ₂ t+θ)]−AB Cos [(w ₁t+φ)+w2t+θ

When the local oscillator and receive signal have the same angularfrequency w₁ this reduces to:

A Sin(w ₁ t+φ)B Sin(w ₁ t+θ)=AB Cos(φ−θ)−AB Cos [(2w ₁ t+θ+φ)]

Which after low pass filtering (shown as LPF in FIG. 7) to remove 2w₁tproduces:

A Sin(w ₁ t+ω)B Sin(w ₁ t+θ)=AB Cos(φ−θ)

In this way, the low pass filtered synchronous phase demodulator outputsignal for any received signal, when correctly synchronized is:

${y_{nm}\mspace{14mu}( {x,t,w_{nm}} )} = {{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Cos}\mspace{14mu} 2\pi\mspace{14mu}( {\varphi + \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{nm}}} )}$

5.1.2.2.2.2.4 FHRG Demodulator Sinc Filter

The smartphone softmodem demodulator utilizes an oversampling andaveraging Sinc filter as the “LPF” of the demodulator module at 706 toremove unwanted components and decimate the demodulated signal to thebaseband sampling rate.

The received frame comprises both tone pairs which are transmittedtogether in the same tone burst (e.g., where two or more pairs of tonesare played at the same time) and also comprises tone hops which aretransmitted in subsequent tone burst times. When demodulating the returnsignal, for example f_(ij) the unwanted demodulated signal must beremoved. These include:

-   -   baseband signals spaced at frequencies: |f_(ij)−f_(nm)|    -   aliased components due to the sampling process at both        |2f_(nm)−f_(s)| and |f_(nm)−f_(s)|    -   demodulated audio frequency components at: 2f_(nm)    -   audio carrier at f_(nm) which might come through the demodulator        due to non-linearity.

To achieve this, a low pass filter LPF (such as a Sinc filter) may beused. The Sinc filter is almost ideal for the purpose as it has a filterresponse with zeros at all the unwanted demodulation components bydesign. The transfer function for such a moving average filter of lengthL is:

${H(\omega)} = {( \frac{1}{L} )\frac{( {1 - e - {j\;\omega\; L}} )}{( {1 - e - {j\;\omega}} )}}$

FIG. 9 illustrates an example of a Sinc filter response, while FIG. 10shows the worst case attenuation characteristics of the 125 Hz Sincfilter due to the averaging of 384 samples over an 8 ms period.

5.1.2.2.2.2.5 Demodulator Baseband Signal

The smartphone softmodem transmits a modulated tone frame. The framecomprises multiple tone pairs (e.g., two tone pairs having four tones)which are transmitted together in the same tone burst and also comprisestone hops which are transmitted in subsequent tone burst times.

First we will discuss the demodulation of a tone pair. Because tonepairs are transmitted, reflected and received simultaneously, their onlydemodulated phase difference is due to tone pair frequency difference.

Before the “time of flight” time:

$t_{tof} = \frac{2d}{v}$

there is no return reflected signal and hence the demodulator outputsfor both tone pair components is a DC level due to near end crosstalkand static reflections.

After the “time of flight” time this DC level receives a contributionfrom the moving target component. The demodulator output now receivesas:

-   a) For the in-phase (I) demodulator signal output for each tone pair    frequency we get:

${I_{11}\mspace{14mu}( {x,t,w_{11}} )} = {\Sigma\mspace{14mu}{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Cos}\mspace{14mu} 2\pi\mspace{14mu}( \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{11}} )}$${I_{12}\mspace{14mu}( {x,t,w_{11}} )} = {\Sigma\mspace{14mu}{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Cos}\mspace{14mu} 2\pi\mspace{14mu}( \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{12}} )}$

-   b) For the quadrature (Q) demodulator signal output for each tone    pair frequency we get:

${Q_{11}\mspace{14mu}( {x,t,w_{11}} )} = {\Sigma\mspace{14mu}{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Sin}\mspace{14mu} 2\pi\mspace{14mu}( \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{11}} )}$${Q_{12}\mspace{14mu}( {x,t,w_{11}} )} = {\Sigma\mspace{14mu}{Ae}\mspace{14mu}{\sigma( {1 - \alpha} )}\frac{P_{0}}{( {4\pi} )^{2}d^{({\beta + \gamma})}}{Sin}\mspace{14mu} 2\pi\mspace{14mu}( \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{12}} )}$

The summation Σ of each of these samples in the demodulated tone burstreceive signal results in “Sinc” filtering of the frequencies. Thisfilter is designed to average the required receive tone burst to enhancethe signal to noise and produce zeros in the transfer function at theunwanted demodulated tone burst frequencies spaced every f_(nm)−f₁₁ inorder to reject all these frequencies.

This summation occurs during the full tone burst period but thecomponent due to movement only occurs from after the time of flightperiod until the end of the tone pair burst period, i.e.:

${{From}\mspace{14mu} t_{tof}} = {{\frac{2d_{0}}{v}\mspace{14mu}{until}\mspace{14mu} t_{p}} = \frac{1}{f_{11} - f_{12}}}$

As result of the time of flight a further signal attenuation factor isintroduced into the signal recovery. The attenuation factor is:

$\frac{t_{p} - t_{tof}}{t_{p}} = {1 - \frac{d}{D}}$

which is a linear decrease in amplitude up to maximum range D.

The demodulation of the audio signal and subsequent averaging at theframe rate when the frame comprises n sequential tone pairs produces abaseband signal with a sample rate of:

$\frac{1}{n\mspace{14mu} t_{p}}$

samples per second and provides 2n different I and Q basebanddemodulated signals.5.1.2.2.2.2.6 Demodulator I_(mn) and Q_(mn)

The result of this demodulation and averaging operation is thedemodulation of the tone pair audio frequencies to four separatebaseband signal samples, namely I₁₁, I₁₂, Q₁₁, Q₂₂, at the sampling rateof:

$\frac{1}{n\mspace{14mu} t_{p}}$

samples per second.

During subsequent periods of the frame this operation is repeated fordifferent frequencies which are separated in frequency from the previouspair by 1/t_(p) to t_(P) maintain optimum rejection by the Sinc filterfunction.

This sampling procedure is repeated every frame to generate amultiplicity of baseband signals. In the example given, it produces2×4×I and 2×4×Q baseband signals, each with their own phasecharacteristic due to the trigonometric identity:

${Cos}\mspace{14mu} 2{\pi( \frac{{2d_{0}} + {2A_{b}\mspace{14mu}{Sin}\mspace{14mu}( {{2\pi\; f_{b}\mspace{14mu} t} + \theta} )}}{\lambda_{nm}} )}$

An example of the phase change by the first tone pair at 1 m with amoving target is depicted in FIG. 11.

The AFHRG architecture provides for tight timing synchronizationfacilitated by the time, frequency, and envelope amplitudecharacteristics of the tone pair such as with a frame synchronizationmodule 703. Audio Tx and Rx frames may be asynchronous. Uponinitialization, the mobile device might be expected to maintain perfectTx-Rx synchronization. According to some aspects of the disclosure, andas shown in FIG. 12, initial synchronization may be achieved with atraining tone frame, which may comprise nominal frame tones, as shown at1202. The Rx signal may be IQ demodulated by IQ demodulator 706, asshown above with respect to FIG. 7. The envelope may then be computed(e.g., via taking the absolute value, followed by low pass filtering, orusing a Hilbert transform), as shown at 1204, and the timing extractedusing level threshold detection, as shown at 1206. The shape of the ASin(ω₁t)−A Sin(ω₂t) pulse assists auto correlation and may be configuredto improve precision and accuracy. The Rx circular buffer index may thenset for the correct synchronized timing, as shown at 1208. In someaspects of the disclosure, a training tone frame may not be used;rather, the Rx may be activated followed by a short period of timebefore the TX is activated, and a threshold may be used to detect thestart of the TX frame after the short recorded silence period. Thethreshold may be selected to be robust to background noise, and priorknowledge of the Tx signal rise time may be used to create an offsetfrom true Tx start, and thus correct for the threshold detection thatoccurs during said rise time. When considering the estimated envelope ofthe signal, peak and “trough” (where the trough is around the zero line,due to the absolute value being taken) fiducial points detection may beperformed, using a detection algorithm that may adapt based on therelative signal levels. Optionally, only the peaks may be processed, asthe troughs may be more likely to contain noise due to reflections.

In some cases, a device (such as a mobile device) may losesynchronization due to other activity or processing causing a jitter ordrop in audio samples. Accordingly, a resynchronization strategy may berequired, such as with the frame synchronization module 703. Accordingto some aspects of the disclosure, introduction of periodic trainingsequences may be implemented to regularly confirm that synchronizationis good/true. An alternative approach (which may optionally also makeuse of such periodic training sequences, but does not such use), is tocross correlate a known Tx frame sequence (typically a single frame)along a segment of Rx signal iteratively until a maximal correlationabove a threshold is detected. The threshold may be chosen to be robustto the expected noise and multipath interference in the Rx signal. Theindex of this maximal correlation may then be used as an index toestimate the synchronization correction factor. It is noted that ade-synchronization in the demodulated signal may appear as a small orprofound step (usually the latter) in the baseline. Unlike the near stepresponse seen in some real large motion signals, the de-synchronizedsegment does not hold useful physiological information, and is removedfrom the output baseband signal (which may lead to the loss of somenumber of seconds of signal).

Thus, synchronization typically makes use of prior knowledge of thetransmit signal, with initial alignment being performed through atechnique such as envelope detection, followed by selection of thecorrect burst utilizing cross-correlation between received signal Rx andknown transmit signal Tx. Loss of synchronization checks should ideallybe performed regularly, and optionally include data integrity tests.

An example of such an integrity test, for the case of a newsynchronization being performed, a check may be performed to compare thenew synchronization with the timing of one or more previoussynchronization(s). It would be expected that the time differencebetween candidate synchronization times in samples would be equal to aninteger number of frames within an acceptable timing tolerance. If thisis not the case, then a likely loss of synchronization has occurred. Insuch a case, the system may initiate a re-initialization (e.g., with anew training sequence (that is unique in the local time period), theintroduction of a defined period “silence” in Tx, or some other marker).In such cases, data sensed since the potential de-sync event may beflagged as questionable and may potentially be discarded.

Such de-sync checking may be performed continually on the device, andbuild up useful trend information. For example, if regular de-syncs aredetected and corrected, the system may adapt the audio buffer lengths tominimize or stop such undesirable behavior, or make changes toprocessing or memory load, such as deferring some processing to the endof a sleep session, and buffering data for such processing rather thanexecuting complex algorithms in real or near real-time. It can be seenthat such re-synchronization approaches are valid and useful for avariety of Tx types (FHRG, AFHRG, FMCW etc.)—especially when utilizing acomplex processor such as a smart device. As described, the correlationmay be performed between the envelope of the reference frame andestimate envelope of the Rx sequence, although, the correlation couldalso be performed directly between a reference frame and an Rx sequence.Such correlation can be performed in the time domain or in the frequencydomain (e.g., as a cross spectral density or cross coherence measure).There are also circumstances where the Tx signal stops beinggenerated/playing for a period of time (when it is expected to beplaying or due to a user interaction such as selecting another app on asmart device, or receiving a call), and re-synchronization is pauseduntil the Rx sees a signal above a minimal level threshold. Potentially,de-sync could also occur over time if a device's main processor andaudio CODEC are not perfectly synchronized. For suspected longerduration de-sync, a mechanism to generate and play a training sequencemay be used,

Note that a de-sync can produce a signal that looks like a DC shiftand/or step response in the signal, and the mean (average) level ortrend may change after a de-sync event. Furthermore, a de-sync may alsohappen due to external factors such as a loud noise (e.g., a briefimpulse or sustained period) in the environment; for example, a loudbang, something knocking against the device containing the mic or anearby table, very loud snoring, coughing, sneezing, shouting etc. couldcause the Tx signal to be drowned out (noise sources have similarfrequency content to Tx, but at higher amplitude), and/or the Rx to beswamped (going in to saturation, hard or soft clipping, or potentiallyactivating the automatic gain control (AGC)).

FIG. 13 illustrates an example of a 32 ms frame with intermediatefrequency. According to some aspects of the disclosure, the IF stage maybe configured to compare early 4 ms (0.5 m range) signals with later 4ms signals. In accordance with other aspects of the disclosure, the IFstage may be configured to compare the phase of a first tone with thephase of a second tone. Because the IF stage compares the level or phasechange in the receive signal within the time of flight, common modesignal artefacts such as motion and 1/f noise may be reduced or removed.

Room reverberation can produce room modes at resonant frequencies. TheAFHRG architecture allows for the shift of the fundamental frequenciesonce the frame frequency separation is maintained. The pulse pairfrequencies may be shifted to a frequency that does not produce roommodes. In other embodiments, the frame pulse pair frequencies may behopped to lessen the build-up of mode energy. In other embodiments, thefame pulse pair frequencies may be hopped using a long, non-repetitivepseudorandom sequence, throughout the frequency specific reverberationtime to prevent the reflections being seen by the homodyne receiver. Theframe frequency may be dithered to alleviate interference from modes, ora sinusoidal frame shift may be used.

Use of an AFHRG system provides many advantages. For example, such asystem allows for adaptable transmit frequencies to mitigate room modes.The system uses active transmitting with a repeated pulse mechanism anddifferent frequencies to improve SNR, unlike a typical “quiet period”required in say a pulsed continuous wave RADAR system. In this regard,subsequent tone pairs at different frequencies from a prior tone pair ina frame implements a “quiet period.” The subsequent frequencies allowsettling of the propagation of the reflected sound from the earlierdifferent frequency tone pair while the subsequent tone pairs are inoperation (propagating). Time slots provide range gating to a firstorder. Moreover, the architecture uses dual frequency pulses to furtherimprove SNR. Inclusion of an intermediate frequency phase provides fordefined and/or programmable range gating. The architecture is designedto allow for slow sampling periods using Costas or pseudorandomfrequency hopping code to mitigate room reverberation. In addition, theorthogonal Dirac Comb frequencies also allow for frame timing ditheringand tone frequency dithering to further assist noise reduction.

It should be noted that a wider or a narrower bandwidth could also bechosen in AFHRG. For example, if a particular handset can transmit andreceive with good signal strength up to 21 kHz, and the subject usingthe system can hear up to 18 kHz, a 2 kHz bandwidth of 19-21 kHz mightbe selected for use by the system. It can also be seen that these tonepairs can be hidden in other transmitted (or detected ambient) audiocontent, and adapt to changes in the other audio content to remainmasked from the user. The masking may be achieved by transmittingaudible (e.g., from, for example, about 250 Hz upwards) or inaudibletone pairs. Where the system is operating at, for example, about over 18kHz, then any generated music source may be low pass filtered belowabout 18 kHz; conversely, where the audio source cannot be directlyprocessed, then the system tracks the audio content, and injects apredicted number of tone pairs, and adapts to both maximize SNR, andminimize audibility. In effect, elements of the existing audio contentprovide the masking for the tone pair—where the tone pair(s) are adaptedto maximize the masking effect; the auditory masking is where theperception of the tone pair is reduced or removed.

An alternate approach when generating or playing an acoustic signal(e.g., music) to using (A)FHRG, FMCW, UWB etc. (with or without masking)is to directly modulate the carrier. In such a case, the sensing signalis directly encoded in the played signal by adjusting the signal content(specifically by varying the amplitude of the signal). The subsequentprocessing comprises demodulating the incoming (received) signal vs.outgoing (generated/transmitted) signal by mixing and then low passfiltering to get baseband signal. The phase change in the carrier signalis proportional to movement such as breathing in the vicinity of thesensor. One caveat is if the music naturally changes amplitude orfrequency at the breathing frequency due to an underlying beat, thenthis can increase noise on the demodulated signal. Also, in quiteperiods in the playback, an extra signal may need to be injected (suchas amplitude modulated noise) in order to continue detecting breathingduring this time, or the system may simply ignore the baseband signalduring such quiet playback periods (i.e., in order to avoid the need togenerate a ‘filler’ modulated signal). It can also be seen that insteadof (or in addition to) amplitude modulating an audio signal (such as amusic signal), it is possible to phase code the signal with definedphase segments, and then demodulate the received signal, and track thephase change in the received signals during the encoded intervals, inorder to recover a baseband signal.

5.1.2.2.2.3 Adaptive Time of Flight

According to some aspects of the disclosure, an Adaptive Time of Flight(AToF) architecture can be used to mitigate room acoustic issues. TheAToF architecture is similar to AFHRG. Like AFHRG, AToF uses thenormally quiet period to enhance SNR by repeating the pulses. Forexample, four individual homodyne pulsed sonar signals could betransmitted in each frame. Each signal may have an 8 ms time of flight.At 8 ms time of flight, the range, including both directions, is 2.7meters giving an effective actual frame of 1.35 m, and a pulserepetition frequency of 31.25 Hz. Additionally, each pulse may includetwo pulse frequencies, as shown in FIG. 14, resulting in 8 separatepulsed homodyne signals in a single 32 ms transceiver frame. Unlike theAFHRG architecture, AToF uses pulses that last 1 ms instead of 8 ms.

Like AFHRG, AToF may use Dirac Comb features and orthogonal pulse pairsto help shape the pulse. AToF may use separate frequency pairs for eachtimeslot and may use linear or Costas code frequency hopping. Thetimeslot may be determined by the range detection required. Frequencypairs may be generated as {A Sin(ω₁t)−A Sin(ω₂t)} with

${\omega_{1} - \omega_{2}} = {\frac{2\pi}{1\mspace{14mu}{ms}}.}$

Each frame may be adapted, by adjusting its frequency inside a fixedband, to mitigate fading once the requirements of the frequency pairsequation is met.

In summary, some advantages of frequency pairs (versus say multipleindividual tones at different frequencies) include:

-   -   Frequency flexibility: It allows any frequency to be used in any        timeslot time

$( {{{{once}\mspace{14mu} f_{1}} - f_{2}} = \frac{1}{t_{ts}}} )$

which enables frequency shifting adaptability to mitigate room modes andfading

-   -   S/N (impact on SNR): Increases the signal to noise as result of        improved bandwidth utilization (with allowance for scaling of        the edges of the pulse)    -   Transient mitigation: Provides a near ideal zero crossing        transition which mitigates frequency hopping transients at both        transmitter and receiver and allows for true pseudo random        frequency hopping capability. This leads to an inaudible system.    -   Facilitates synchronization recovery: The unique frequency and        amplitude profile of the shaped pulse improves synchronization        detection and accuracy (i.e., there is a clear shape)    -   Pulse and frame timing flexibility: Allows for any timeslot time        duration to facilitate a “variable detection range” feature    -   Facilitates dithering: Enables both frequency dithering and time        slot dithering to mitigate noise    -   Tight BW: Naturally produces a very tight bandwidth and so is        inaudible even without filtering (filters can introduce phase        distortion)    -   MultiTone: It allows the use of multiple tones in the same        timeslot to enhance S/N and mitigate fading.

5.1.2.2.2.4 Frequency Modulated Continuous Wave (FMCW)

In yet another aspect of the disclosure, a frequency modulatedcontinuous wave (FMCW) architecture may be used to mitigate some of thetechnical challenges described above. FMCW signals are typically used toprovide localization (i.e., distance as well as speed) as FMCW enablesrange estimation and thus provide range gating. An example of an audiblesawtooth chirp in daily life is like a chirp from a bird, whereas atriangle chirp might sound like a police siren, for example. FMCW may beused in RF sensors, and also acoustic sensors, such as implemented on atypical smart device such as a smartphone or tablet, using inbuilt orexternal loudspeaker(s) and microphone(s).

It should be noted that audible guide tone(s) (e.g., played at the startof a recording, or when a smart device is moved) can be used to conveythe relative “loudness” of inaudible sounds. On a smart device with oneor more microphones, a profile is selected to minimize (and, ideally,make unnecessary) any extra processing in software or the hardware CODECsuch as to disable echo cancellation, noise reduction, automatic gaincontrol etc. For some handsets, the camcorder mic or main microphoneconfigured to be in “voice recognition” mode may provide good results,or the selection of an “unprocessed” mic feed such as one that might beused for virtual reality applications or music mixing. Depending on thehandset, a “voice recognition” mode such as available in some Android OSrevisions may disable effects and pre-processing on the mic (which isdesirable).

FMCW allows tracking across a spatial range, so determination of where abreathing person is, if they have moved, and to separate two or morepeople breathing when in the range of the sensor (i.e., a breathingwaveform from each subject at a different range can be recovered).

FMCW can have a chirp, such as a ramp sawtooth, triangle, or sinusoidalshape. Thus, unlike pulses of an (A)FHRG type system, the FMCW systemmay generate a repeated waveform of sound (e.g., inaudible) that haschanging frequencies. It is important to match frequency changes at zerocrossings of the signal if possible—i.e., to avoid jump discontinuitiesin the generated signal that can give rise to unwanted harmonics thatmay be audible and/or unnecessarily stress the speaker. The rampsawtooth may ramp up (from lower to higher frequencies) like the form ofthe ramp illustrated in FIG. 15B. When repeated, the ramp up forms anupward saw, by repeating the lower to higher, lower to higher, etc.waveform (ramp sawtooth). However, such a ramp may alternatively rampdown (from higher to lower frequencies). When repeated, the ramp downforms a downward saw, by repeating the higher to lower, higher to lower,etc. waveform (inverted ramp sawtooth). Moreover, although such rampingmay be approximately linear using a linear function as illustrated, insome versions, the rise in frequency may be form a curve (increasing ordecreasing) such as with a polynomial function between the low and high(or the high and low) of the frequency change of the waveform.

In some versions, the FMCW system may be configured to vary one or moreparameters of a form of the repeated waveform. For example, the systemmay vary any one or more of (a) a location of a frequency peak in arepeated portion of the waveform (e.g., peaking earlier or later in therepeated portion). Such a varied parameter may be a change in slope of afrequency change of the ramp of the waveform (e.g., the slope up and/orthe slope down). Such a varied parameter may be a change in thefrequency range of a repeated portion of the waveform.

A particular method to achieve such an inaudible signal for use on asmartdevice using an FMCW triangle waveform is outlined here.

For an FMCW system, the theoretical range resolution is defined as:V/(2*BW), where V is velocity, such as for sound, and BW is bandwidth.Therefore, for V=340 m/s (at say room temperature) and an 18-20 kHzchirp FMCW system, an 85 mm range resolution for separating targets ispossible. Each of one or more moving targets (such as the breathingmovement of a subject) can then be detected with much finer resolutionindividually (similar to a CW system), assuming that (in the case ofthis example), each subject is separated by at least 85 mm in the fieldof the sensor.

Depending on the relative sensitivity to frequency of speaker and/ormicrophone, the system may optionally use emphasis on the transmittedsignal Tx FMCW waveform in order that each frequency is corrected tohave the same amplitude (or other modification to the transmit signalchirp in order to correct for non-linearities in the system). Forexample, if the speaker response decreases with increasing frequency, a19 kHz component of the chirp is generated at higher amplitude than an18 kHz component, such that the actual Tx signal will have the sameamplitude across the frequency range. It is of course important to avoiddistortion, and the system may check the received signal for an adjustedtransmit signal, in order to determine that distortion (such asclipping, unwanted harmonics, jagged waveform etc.) does not occur—i.e.,to adjust the Tx signal such as to be as close to linear as possible, atas large an amplitude as possible, to maximize SNR. A system as deployedmay scale down the waveform and/or reduce volume to meet a target SNR toextract respiratory parameters (there is a tradeoff between maximizingSNR and driving the loudspeaker(s) hard)—while maintaining as linear asignal as feasible.

Based on channel conditions, the chirp may also be adapted by thesystem—e.g., to be robust to strong interfering sources, by adapting thebandwidth used.

5.1.2.2.2.5 FMCW Chirp Types, and Inaudibility

FMCW, at audible frequencies, has a period of 1/10 ms=100 Hz (andassociated harmonics) that is clearly audible as buzzing sound unlessfurther digital signal processing steps are performed. The human ear hasan incredible ability to discern very low amplitude signals (especiallyin a quiet environment such as a bedroom), even if high pass filteringremoves components down to −40 dB (filtering of unwanted components tobelow −90 dB may be required). For example, the start and end of thechirp can be de-emphasized (e.g., to window the chirp with a Hamming,Hanning, Blackman etc. window), and then re-emphasized on subsequentprocessing in order to correct.

It is also possible to isolate the chirp—e.g., only repeat every 100-200ms to introduce a quiet period between chirps that is much longer than asingle chirp in duration; this can have a side benefit of minimizing thedetection of reverberation related standing waves. A downside of thisapproach as less of the available transmit energy is used versus acontinuous repeating chirp signal. A trade-off is that reducing theaudible clicks and reducing the continuous output signal does have somebenefits in terms of driving the device (e.g., a smart device such as aphone loudspeaker) loud speaker and amplifier less hard; for example, acoil loud speaker may be impacted by the transients if driven at maximumamplitude over a long period of time.

FIG. 15 illustrates an example of an audible version of an FMCW rampsequence. FIG. 15(a) illustrates a standard chirp in a graph showingamplitude versus time. FIG. 15(b) shows a spectrogram of this standardaudible chirp, while FIG. 15(c) illustrates a frequency spectrum of astandard audio chirp.

As described above, an FMCW tone can also use a sinusoidal profilerather than a ramp. FIG. 16 illustrates an example of an FMCW sinusoidalprofile with high pass filtering applied. FIG. 16A shows the signalamplitude verses time. FIG. 16 B shows a frequency spectrum of thesignal. FIG. 16C shows a spectrogram of signal. The following exampleuses a start frequency of 18 kHz and end frequency of 20 kHz. A “chirp”of 512 samples is generated with a sinusoidal profile. The middlefrequency is 19,687.5 Hz and the deviation is +/−1,000 Hz. A sequence ofthese chirps has continuous phase across chirp boundaries which helps tomake the sequence inaudible. A high pass filter can optionally beapplied to the resulting sequence; however, it should be noted that thisoperation can give rise to phase discontinuities, which paradoxicallymake the signal more audible rather than, as desired, less audible. TheFFT of the shifted receive signal is multiplied by the conjugate of theFFT of the transmit sequence. The phase angle is extracted andunwrapped. The best fitting straight line is found.

As noted, if the sinusoidal signal had to be filtered in order to makeit inaudible, this is not ideal as it can distort the phase information.This may be due to phase discontinuity between sweeps. Therefore, aninaudible FMCW sequence using a triangular waveform may be used.

A triangular waveform signal that is a phase continuous waveform can beproduced so that clicks are not produced by a speaker(s) thatgenerate(s) the waveform. Despite being continuous across sweeps, phasedifferences at the beginning of each sweep may be present. The equationsmay be modified to ensure the phase at the beginning of the next sweepstarted at a multiple of 2π, allowing looping of a single section of thewaveform in a phase continuous manner.

A similar approach can be applied to ramp chirp signals, although thefrequency jump discontinuity from 20 kHz to 18 kHz (assuming a chirp of18 kHz to 20 kHz) can stress the amplifier and loudspeaker in commoditysmart devices. The triangular waveform offers more information than theramp due to addition of the down-sweep, and the triangular waveform'smore “gentle” change in frequency should be less harmful to the phoneshardware than the discontinuous jump of the ramp.

Example equations that may be implemented in a signal generation modulefor generating this phase continuous triangular waveform are as follows.The phase of the triangular chirp can be calculated for both up and downsweep and for time index n from the following closed expressions:

${{triPhase}_{up}(n)} = {\frac{2\pi}{f_{s}}( {{\frac{f_{2} - f_{1}}{N - 2}n^{2}} + {\frac{f_{2} + {( {N - 3} )f_{1}}}{N - 2}n}} )}$${{triPhase}_{dn}(n)} = {{\frac{2\pi}{f_{s}}( {{\frac{f_{1} - f_{2}}{N - 2}n^{2}} + {\frac{{( {N - 3} )f_{2}} + f_{1}}{N - 2}n}} )} + {{triPhase}_{up}( {\frac{N}{2} - 1} )} + \frac{2\pi\; f_{2}}{f_{s}}}$

where

-   -   f_(s) is the sample rate    -   f₁, f₂ are the lower and upper frequencies, respectively    -   N is the total number of samples in the up and down sweeps, i.e.

$\frac{N}{2}$

samples per sweep (assuming even N).

The final phase at the end of the down sweep is given by

${triPhaseFinal} = {\frac{\pi}{f_{s}}( {{Nf}_{2} + {( {N - 2} )f_{1}}} )}$

To bring the sin of the phase back around zero at the start of the nextsweep we provide:

${{triPhaseStart} = {{{triPhaseFinal} + \frac{2\pi\; f_{1}}{f_{s}}} = {2\pi\; m}}},{ {{for}\mspace{14mu}{some}\mspace{14mu}{integer}\mspace{14mu} m}\Rightarrow{N( {f_{2} + f_{1}} )}  = {2{mf}_{s}}}$

For example, assume N and f₂ are fixed. We therefore select m to bringf₁ as close to 18 kHz a possible:

$f_{1} = {\frac{2{mf}_{s}}{N} - f_{2}}$

For N=1024 and f₂=20 kHz, m=406, then f₁=18,062.5 kHz

The demodulation of this triangular waveform can be considered as eitherdemodulating the triangular up and down sweep individually, or byprocessing the up and down sweep simultaneously, or indeed by processingframes of multiple triangular sweeps. It is noted that the case of justprocessing the up-sweep is equivalent to processing a single frame of aramp (sawtooth).

By using the up and/or down sweep, such as in respiration sensing,inspiration can be separated from expiratory parts of the breath (i.e.,know at a point in time if inspiration or expiration is occurring).Examples of triangular signals are shown in FIG. 17 and the demodulationof a detected breathing waveform are shown in FIGS. 18A and 18B. Asshown in FIG. 17, a smart device microphone has recorded the inaudibletriangle emitted by the same smart device's loudspeaker. In FIG. 18A, agraph shows a result of demodulation of a reflection of the triangularsignal in relation to the up sweep of the signal, showing the extractedbreathing waveform at 50.7 cm. FIG. 18B is a graph that shows a resultof demodulation of a reflection of the triangular signal in relation tothe down sweep of the signal, illustrating the extracted breathingwaveform at 50.7 cm. As shown in these figures, the recovered signal ofFIG. 18B appears flipped upside down with respect to the demodulatedunwrapped signal of the corresponding up-sweep due to the difference inphase.

An asymmetric “triangular” ramp may also be considered in place of thesymmetric triangular ramp, where the upsweep is longer duration that thedownsweep (or vice versa). In this case, the shorter duration is (a) tomaintain inaudibility that could be compromised by the transient in sayan upsweep-only ramp, and (b) provide a reference point in the signal.The demodulation is performed on the longer upsweep (as it would be fora sawtooth ramp with a quiet period (but instead the “quiet period” isthe shorter duration downsweep); this potentially allows a reduction inprocessing load (if required), while maximizing the Tx signal used, andmaintaining an inaudible, phase continuous Tx signal.

5.1.2.2.2.6 FMCW Demodulation

An example flow of a SONAR FMCW processing methodology is shown in FIG.19. This illustrates blocks or modules providing front end transmitsignal generation at 1902, signal reception at 1904, synchronization at1906, down conversion at 1908, “2D” analysis at 1910 includinggeneration of signals or data representing any one or more of estimationof activity, movement, presence/absence and respiration. From thesedata/signals, wake and/or sleep staging at 1912 may be provided.

It is possible to correlate the transmitted chirp with the receivedsignal, particularly for checking synchronization in the module at 1906.As an example, the resulting narrow pulse may be useful for determiningfine detail due to the sharp peak after the correlation operation. Thereis generally a reciprocal relationship between the width of a signalsspectrum and the width of the correlation function; e.g., the relativelywide FMCW signal correlated with an FMCW template gives rise to a narrowcorrelation peak. As an aside, correlating a standard chirp with theresponse at the receiver, provides an estimate of the echo impulseresponse.

In FMCW, the system effectively considers the ensemble of all responsesacross the frequency range, as the “strongest” overall response (e.g.,while some frequencies in a chirp may encounter severe fading, othersmay provide a good response, and so the system considers the ensemble).

An example methodology of one or more processors, such as in a mobiledevice, to recover the baseband signal (e.g., gross body movement orbreathing) as an FMCW system is as follows. According to some aspects ofthe disclosure, a chirp sequence may be transmitted such as using a FMCWtransmission generation module at 1902 operating one or more speaker(s).The chirp sequence may be, for example, an inaudible triangular acousticsignal. An incoming received signal, such as one received by a receptionmodule at 1904 operating with one or more microphones, may besynchronized with the transmit signal using, for example, the peakcorrelation described above. Continuous resynchronization checking on achirp or block (e.g., of several chirps) basis may be carried out suchas with a synchronization module at 1906. A mixing operation, such as bymultiplication or summing, may then be performed for demodulation suchas with down conversion module at 1908, wherein one or more sweeps ofthe received signal may be multiplied by one or more sweeps of thetransmit signal. This produces frequencies at the sum and difference ofthe transmit and received frequencies.

One example of a signal transmitted from the phone using a transmissionmodule at 1902 is a triangular chirp that is phase continuous (to ensureinaudibility) and designed to enable looping sound data by a processorcontrolled speaker(s) according to a module running on a computingdevice (e.g., mobile device). An exemplar triangular chirp uses 1500samples at 48 kHz for both the up and down sweep. This gives an up-sweeptime of 31.25 ms (which may be the same for the down-sweep) and providesa baseband sample rate (as opposed to audio sample rate) of 32 Hz ifboth up and down sweep are used consecutively or 16 Hz if they areaverage (or only alternate sweeps used).

Where more than one sweep is used (e.g., 4 sweeps), the calculation maybe repeated on a block basis by moving one sweep per iteration (i.e.,introducing an overlap). In this case, the peaks of the correlation canoptionally be estimated by extracting the envelope of the correlation,using methods such as filtering, max hold, or other methods such as theHilbert transform; outliers can also be removed—e.g., by removing thosefalling outside one-standard deviation from the average of the relativepeak locations. It is possible to use the mode of the relative peaklocations (with outliers removed) to determine the starting index whenextracting each sweep of the received waveform.

The reason that a correlation metric may sometimes be lower than otherscould be due to intermittent unwanted signal processing impacting thetransmitted waveform (causing a diminution or “dipping” of the chirp),or indeed loud sounds in the room environment causing the signal to be“drowned out” for a short time.

A more fine-grained phase level synchronization can be performed (albeitwith greater computational complexity) with a synchronization module at1906 by checking correlation against a template shifted in phase indegree increments up to a maximum of 360 degrees. This estimates thephase level offset.

Unless the timing of the system is very accurately controlled (i.e.,having knowledge of delay in the system down to a sample level), asynchronization step is desirable to ensure the demodulation workscorrectly, and does not produce a noisy or erroneous output.

As previously noted, the down conversion processing in a module at 1908may produce a baseband signal for analysis. With the transmit andreceived data synchronized, the down conversion can now be carried out.This module processes synchronized transmit and receive signals(produced sound and reflected sound) with various submodules orprocesses to extract a “beat” signal. One example of this audioprocessing is illustrated in FIG. 20; in this, I and Q transmit signals,with a phase offset determined using the fine-grained synchronizationmethod may be generated for mixing. A “nested loop” can be repeated at2002 used for the mixing such as in a module or processing at 2004: theouter loop iterates over every sweep in the current received waveformheld in memory and applied by the access process at 2006 to the mixingat 2004 (each up and down chirp are considered a separate sweep); theinner loop then iterates for the I channel first followed by the Qchannel.

Another example of the system, not shown in FIG. 20, optionally buffersa number of received sweeps, calculates the median, and provides amoving median figure of the buffer containing the multiple sweeps. Thismedian value is then subtracted from the current sweep in order toprovide another method of detrending the signal.

For each sweep (either up or down) the relevant portion of the receivedwaveform is extracted (using sync sample index as reference) in order tobe mixed at 2004 with the phase shifted transmit chirp. With bothtransmit and receive section generated, the waveforms are mixed (i.e.,multiplied) together at 2004. It can be seen for example that for thedown-sweep it is possible to mix the received down sweep with TXdown-sweep, a flipped received down-sweep with the TX up-sweep or aflipped received down-sweep with a flipped TX down-sweep.

Output of the mixing operation (e.g., multiplication of the receivedwaveform by the reference aligned waveform (e.g., reference chirp)) maybe low-pass filtered to remove higher sum frequencies such as in afiltering process or module at 2008. As an example, an implementationmay use a triangular chirp 18-20-18 kHz, with a sampling rate of 48 kHz;for such frequency band, the higher sum frequencies in the mixingoperation are actually under-sampled, and produce an aliased componentat about 11-12 kHz (for Fs=48 kHz, the sum is around 36 kHz). The lowpass filter may be configured to ensure that this aliased component isremoved if it occurs in a particular system realization. The componentsof the remaining signal will depend on whether the target is static ormoving. Assuming an oscillating target at a given distance (e.g., abreathing signal), the signal will contain a “beat” signal and a Dopplercomponent. The beat signal is due to the difference in frequencyposition of the transmit and received sweeps caused by the time delayedreflection from the target. The Doppler is a frequency shift cause bythe moving target. Ideally, this beat should be calculated by onlyselecting the section of the sweep from the point the received signalarrives in, to the end of the transmitted sweep. However, this isdifficult to do in practice for a complex target such as a person. Thus,it is possible to use the whole sweep. According to some aspects of thedisclosure, a more advanced system may utilize an adaptive algorithmthat uses the fact that once the subject location is identified clearly,the appropriate portion of the sweep is taken, further increasing thesystem accuracy.

In some cases, the mixed signal may optionally have the mean (average)removed (mean detrended) such as in the detrending processing at 2010and/or be high pass filtered, such as in the HPF processing at 2012, toremove unwanted components that could ultimately manifest in thebaseband. Other detrending operations may be applied to the mixed signalsuch as linear detrending, median detrending etc. The level of filteringapplied at 2008 can depend on the quality of the transmit signal, thestrength of the echoes, number of interferers in the environment,static/multipath reflections and so forth. For higher qualityconditions, less filtering may be applied, as the low frequency envelopeof the receive signal can contain respiration and other movementinformation (as well as in the eventual demodulated baseband signals).For low quality, more challenging conditions, noise in the mixed signalcan be significant, and more filtering is desirable. Indications ofactual signal quality (e.g., of respiration signal quality) can be fedback to these filtering processes/modules, as a feedback signal/data, toselect an appropriate level of filtering at this filtering module stageat 2008.

Two dimensional (2D) analysis at 1910 of the complex FFT matrixsubsequent to down conversion at 1908, is used in order to extractrespiration, presence/absence, gross body movement and activity. Thus,the down converter may produce a frequency domain transformation matrix.For this type of process, the blocks of mixed (and filtered) signal arewindowed (e.g., using a Hanning window module at 2014. Then a Fouriertransform (such as an fast Fourier transform or FFT) at 2016 is carriedout to estimate and produce a “2D” matrix 2018. Each row is the FFT of asweep and each column is an FFT bin. It is these FFT bins that translateinto range, and are therefore referred to as “range bins.” The matrix,such as a set of quadrature matrices with each matrix based on either Ichannel or Q channel information, is then processed by the 2D analysismodule.

An example of such processing is illustrated in reference to the modulesshown in FIG. 21. As part of this 2D analysis at 1910, body (includinglimb) movement and activity detection can be carried out on these data.Movement and activity estimation of the processing module(s) at 2102extracts information about a subject's movement and activity fromanalysis of the complex (I+jQ) mixed signal in the frequency domain. Itmay be configured to operate on the basis that whole body (e.g.,rolling, or movement of limbs) movement is uncorrelated and grosslylarger than chest displacement during respiration. Independent (ordependent on) this movement processing, multiple signal quality valuesare calculated for each range bin. These signal quality values arecalculated for both amplitude and unwrapped phase across both the I andQ channels.

The processing at 2102 produces an “activity estimate” signal and amovement signal (e.g., body movement flag.) The “body movement flag”(BMF) produced by the activity/movement processing at 2102 is a binaryflag indicating if movement has occurred (output at 1 Hz), whereas the“activity count”—, produced in conjunction with counter process moduleat 2104, is a measure of activity between 0 and 30 for each epoch(output at 1/30s). In other words, the “activity count” variablecaptures the amount (severity) and duration of the body movement in adefined period of a 30 seconds block (updating every 30 sec), whereasthe “movement” flag is a simple yes/no to movement occurring that isupdated every second. The raw “activity estimate” that is used forgeneration of the activity count, is a measure estimated on the basisthat the mixed chirp signals are correlated with each other duringnon-movement and uncorrelated during movement periods. As the mixedsweeps are a complex frequency domain representation, a modulus orabsolute value of the signal can be taken before analysis. Pearsoncorrelation is calculated on a series of mixed up-sweeps spaced 4 chirpsapart over the range bins of interest. This spacing corresponds to 125ms for a 1500 sample transmit waveform at 48 kHz. The spacing of chirpsdetermines the velocity profile that is being inspected, and can beadjusted as needed. This correlation output is inverted, e.g., bysubtracting from 1, and so a decrease in correlation relates to anincrease in the metric. The maximum value for each second is calculatedand then passed through a short 3-tap FIR (finite impulse response)boxcar mean filter. With certain devices, the response to movement thatis calculated using this approach can be non-linear in nature. In such acase, the metric can be re-mapped via a natural log function. The signalis then de-trended by subtracting the minimum value observed over theprevious N seconds (corresponding to the maximum observed correlation)and passed into a logistic regression model with a single weight andbias term. This generates the 1 Hz raw activity estimate signal. Thebinary movement flag is generated by applying a threshold to the rawactivity estimate signal. The activity count is generated by comparingthe 1 Hz raw activity estimate, above a threshold, to a non-linearmapping table where a value is chosen from 0 to 2.5 for each second.This is then summed over a 30 second period and limited at a value of 30to generate an activity count for each epoch.

Therefore, a one second (1 Hz) motion flag has been created, along withan activity intensity estimate, as well as an associated activity countto a max value of 30 (summation of activity in a 30 sec epoch) inrelation to the processes of the activity/movement module at 2012 andthe activity counter at 2104.

Another aspect of the 2D processing at 1910 is the calculation of thesubject's distance from the sensor (or several distances in the case oftwo or more subjects in range of the sensor). This may be implementedwith a range bin selection algorithm that processes the resulting twodimensional (2D) matrix 2018 to yield a 1D matrix (or matrices) at thebin(s) of interest. Although not shown in FIG. 21, the output of amodule for such a selection process may inform the processing modules ofthe 2D analysis, including for example, any of the extraction processingat 2016, the calculation processing at 2108 and the respiration decisionprocessing at 2110. In this regard, unwrapping the phase at theresulting beat frequency will return the desired oscillating movement ofthe target (assuming no phase unwrapping problems occur). Optional phaseunwrapping error detection (and potential correction) may be performedto mitigate possible jump discontinuities in the unwrapped signal due togross movements. However, these “errors” can actually yield usefulinformation, i.e., they may be used to detect movements (usually largermovements such as gross motion) in the signal. If the input signals tophase unwrapping are very low amplitude noise only, a flag mayoptionally be set for the duration of this ‘below threshold’ signal asthe unwrapping might be invalid.

The start time and end time, as well as position (i.e., range) of amovement can be detected in the 2D unwrapped phase matrix throughvarious methods; for example, this can be achieved using 2D envelopeextraction and normalization, followed by thresholding. The time scaleof the normalization is configured to be sufficiently large to excludethe variation in phase due to breathing-like movements (e.g., >>k*12seconds for a minimum breathing rate defined as 5 bpm). The detection ofsuch movements can be used as an input trigger to the bin selectionprocess; for example, the bin selection algorithm may “lock” to a priorrange bin during the movement period, and hold that bin, until a validbreathing signal is next detected in or near that bin. This can reducethe computational overhead in the case where a subject is breathingquietly, moves in bed, and then lies quietly; they are still seen in thesame range bin. Thus, start and/or end position of the detected movementcan be used to limit the search range for bin selection. This can applyto multiple subject monitoring use cases as well (e.g., where twosubjects in bed are simultaneously monitored, the movement of onesubject may smear the breathing signal of the other, but such a lock-inbin selection can aid the recovery of the valid range bin (and thisrespiration signal extraction) of both subjects—even during the largemovement of the one subject.

This approach yields the signal produced by the phase-unwrappedoscillation. The complex output yields a demodulated baseband IQ signalcontaining body motion of a living person or animal (includingbreathing) data for subsequent processing. For the example of atriangular Tx waveform, the system may process the linear up-sweep anddown-sweep separately, and consider as a separate IQ pair (e.g., toincrease SNR, and/or to separate inspiration from expiration breaths).In some realizations, optionally the beat frequencies from each(up-sweep and down-sweep) may be processed in order to average outpossible coupling effect in the Doppler signals, or in order to producea better range estimate.

A baseband SNR metric may be configured to compare a respiratory noiseband of 0.125-0.5 Hz (equivalent to 7.5 br/min to 30 br/min—althoughthis might be widened to around 5-40 br/min depending on the usecase—i.e., the primary breathing signal content) to a movement noiseband of 4-8 Hz (i.e., a band primarily containing movement other thanbreathing), where the baseband signal(s) are sampled at 16 Hz or above.Baseband content below 0.083 Hz (equivalent to 5 breaths/min) may beremoved by high pass filtering. Heart rate information may be containedin the band of approximately 0.17-3.3 Hz (equivalent to 25 beats perminute to 200 beats per minute).

During SONAR FMCW processing, optionally the difference between beatestimates may be taken in order to exclude (remove) static signalcomponents, i.e., by subtracting adjacent estimates.

The range bin selection algorithm in this approach may be configured totrack the range bin corresponding to the location of one or moresubjects in the range of the sensor. Depending on the data supplied bythe user on positioning, this may require a partial or complete searchof the possible range bins. For example, if the user notes how far theysleep (or are sitting) with respect to the device, the search range canbe narrowed. Typically, a block or epoch of data under considerationmight be 30 sec long and not overlapping (i.e., one range bin perepoch). Other realizations might use longer epoch lengths, and employoverlapping (i.e., multiple range bin estimates per epoch).

As the detection of a valid breathing rate (or probability of abreathing rate over a defined threshold) is used, this limits thesmallest possible window length as at least one breathing cycle shouldbe able to be contained in the epoch (e.g., a very slow breathing rateof 5 breaths per minute implies one breath every 12 sec). At the upperend of breathing rates, 45-50 br/min is a typical limit. Thus, whenusing a spectral estimate of the epoch (e.g., removing the mean,optionally windowing, and then carrying out an FFT), the peak(s)relating in the desired breathing band (e.g., 5-45 br/min) areextracted, and the power in the band is compared to the full signalpower. The relative power of the maximal peak (or maximal peak-to-meanratio) across bands is compared to a threshold to determine if acandidate breathing frequency is present. In some circumstances, severalcandidate bins with similar breathing frequency may be found. These canoccur due to reflections in the room, giving rise to apparent breathingat multiple ranges (this could also be related to FFT side lobes).

Processing of the triangular waveform can help mitigate such uncertaintyin range (e.g., due to Doppler coupling), although if a reflectioncontains better signal than the direct path for a period of time, thesystem can choose to use this. Where the user has a comforter/duvet, andthe room contains soft furnishings (including say a bed and curtains),it is less likely that non-direct reflections will yield a higher signalto noise ratio than the direct component. As the primary signal may be areflection from the duvet surface, this may appear slightly closer thanthe chest of the person when considering the actual estimated range.

It can be seen that knowledge of a prior epoch's estimate range bin canbe used to inform subsequent range bin searches, in an effort to reduceprocessing time. For a system that does not require near real-time(epoch by epoch) analysis, longer timescales can be considered.

1.1 Signal Quality

The signal quality of (i.e., the detection of, and related quality of)respiration, which may be considered by the filtering at 2008 of FIG. 20as previously discussed, can be determined by way of various signalquality metrics calculated as part of the FFT 2D metrics, as shown inthe calculator processing of the module at 2108 of FIG. 21. Thesematrices may also be considered in a respiration decision processing at2110. Optionally, the 2D analysis processing at FIG. 21 may includeprocessing or modules for determination of the absence/presence or sleepstaging, which are based on the outputs and some intermediate valuesdepicted.

With respect to the calculator processing at 2108, various metrics, suchas for respiration estimation, may be determine. In the example, fourmetrics are indicated in FIG. 21:

1. “I2F”—In-band (I) squared over full band

2. “Ibm—In-band (I) only metric

3. “Kurt”—Metric based on the Kurtosis of the covariance

4. “Fda”—Frequency domain analysis

1. “I2F”

The I2F metric may be calculated as follows (a similar approach appliesfor the Q (quadrature) channel which could be called inBandPwrQ):

${I\; 2F_{I}} = \frac{{inBandPwrI}^{2}}{fullBandPwrI}$

Where “inBandPwrl” is the total power from, for example, about 0.1 Hz to0.6 Hz (e.g., within the respiration band of interest that has beenselected) and “fullBandPwrl” is the total power outside of this range.This metric is founded on a broad assumption that full-band and thesubset of in-band power is similar, and uses the full band power toestimate the noise in-band.

2. “Ibm”

The next metric does not make the same assumption as I2F, and providesan improved estimation of the signal and the noise in-band. It does thisby finding the peak power in-band and then calculating the power aroundthis (for example by taking three FFT bins each side of the peak). Itthen multiplies this signal power by the peak value itself divided bynext peak value (for the case of a broadly sinusoidal type signal).Where respiration contains strong harmonics, the choice of bins can bereevaluated, such that the harmonics are not confused with noisecomponents. The noise estimate then is everything outside the signalsub-band but still within the respiration band:

${Ibm} = \frac{{PwrAroundPk}*{Pk}\text{/}{NxtPk}}{{inBandPwr} - {PwrAroundPk}}$

3. Kurtosis (“KURT”)

Kurtosis provides a measure of “tailedness” of a distribution. This canprovide a means of separating respiration from other non-respirationsignals. For example, a signal quality output can be the inverse of theKurtosis of the covariance of a DC-removed (using an IIR HPF) signalwithin a specified distance from the peak covariance. This metric is setto “invalid” under conditions of poor signal quality.

4. “Fda”

“Fda” (Frequency domain analysis) can be performed; such statistics canbe calculated using 64 second overlapping data windows, with 1 secondstep length. Computations are causal, using retrospective data. Theprocess may detect breathing rates within a certain breathing ratewindow. For example, breathing rates may be detected as described inInternational Application WO2015006364, the entire disclosure of whichis incorporated herein by reference. For example, breathing rates may bedetected within a rate window that amounts to 6 to 40 breaths per minute(bpm), corresponding to 0.1-0.67 Hz. This frequency band corresponds torealistic human breathing rates. Therefore, ‘in-band’ refers to thefrequency range 0.1-0.67 Hz. Each 64 second window may contain 1024 (64seconds at 16 Hz) data points. Hence, the algorithm calculates a 512point (N/2) FFT for each (I and Q) data window. The results of theseFFTs are used to calculate in-band spectral peak (which may subsequentlybe used to determine respiration rate), as described below. The in-bandfrequency range is used to calculate respiration rate for each 64 secondwindow, as described below.

Other types of “Fda” analysis are presented below.

An alternative frequency band can also be considered for typical heartrate (e.g., where a HR of 45 beats per minute to 180 beats per minutecorresponds to 0.75-3 Hz).

The spectral peak ratio may also be determined. The maximum in-band andoutside-band peaks are identified, and used to calculate the spectralpeak ratio. This may be understood to be the ratio of the maximumin-band peak, to the maximum outside-band peak.

The In-band variance may also be determined. The in-band variancequantifies the power in the frequency band. This can also be used forsubsequent presence/absence detection in some cases.

The spectral peak is identified in the frequency band of interestthrough the implementation of a figure of merit which combines spectralpower level at each bin, as well as distance from adjacent peaks andfrequency of bin. The bin with the highest value for the above describedfigure of merit.

As part of the 2D analysis at 1910, the four metrics of the calculatorprocesses at 2108 that have been outlined are calculated in orderidentify the one or more living persons in the range of the sensingvicinity of the mobile device, and track if they move to a differentrange (distance from sensor) or indeed leave the sensing space (go tobathroom) or return to the sensing space. This provides an input intoabsence/presence detection, as well as whether there are interfererswithin the sensing range. These values may then be evaluated further,such as in the processes of the respiration decision processing moduleat 2110 to produce a final respiration estimate for one or more subjectmonitored with the system.

1.2 Absence/Presence Estimation

As illustrated in FIG. 19, the 2D analysis at 1910 may also provideprocessing for absence/presence estimating and an output indication ofsuch an estimate. The detection of body movement of a person, and theirrespiratory parameters, can be used to assess a series of signals todetermine whether a subject is absent or present from the sensor range.Depending on the signal quality detection, an “absence” can be triggeredin 30 secs or less (and clearly distinguished from an apnea).Absence/presence detection can in in some versions be seen to includemethodologies of feature extraction, and subsequent absence detection.An example flow diagram for such a process is shown in FIG. 22, whichmay use some of the calculated features/metrics/signals of thepreviously described processes at 1910, but may calculate others asdescribed herein. Various types of features can be considered indeciding human presence (Prs) and human absence (Abs), including, forexample one or more of:

-   -   1. A non-zero activity count    -   2. Pearson correlation coefficient between the current 2D signal        (respiration vs range) and the previous window—limited to        respiration and sensing-ranges of interest    -   3. The maximum of the I/Q Ibm quality metrics for the current        respiration window    -   4. The maximum variance of I/Q respiration rate channels over a        60 sec window    -   5. The maximum variance of I/Q respiration rate channels over a        500 sec window

The features are extracted from the current buffer and pre-processed bytaking percentiles over the buffer length (e.g., over 60 s). Thesefeatures are then combined into logistic regression model which outputsthe probability of absence occurring for the current epoch. To limitfalse positives, this can for example be averaged over a short window(several epochs) to generate the final probability estimate. For periodsof motion that are deemed related to body motion (motion of a person oranimal), presence is asserted preferentially. In the illustrated exampleof FIG. 22, an activity count, IBM, pearson correlation coefficient anda probability of absence are compared to suitable thresholds to assesswhether a person is present or absent. In this example, any one positiveevaluation may be sufficient to determine presence whereas, each of thetests may be evaluated in the negative to determine absence.

Taking a holistic system view, motion such as limb movement, rollingover in the end etc. will tend to disrupt the breathing signal, givingrise to higher frequency components (but may be separable whenconsidering “2D” processing). FIGS. 23A, 23B and 23C provide an exampleof a segment of 2D data (upper panel), with two subsections (FIG. 23Band FIG. 23C) illustrating human activity in range of the acousticsensing by a computing device with a microphone and speaker. Each traceor signal of the graph represents acoustic sensing of a differentdistance (range) from a mobile device having the application and modulesdescribed herein. In the figures, it is possible to visualize therespiration signal at several distances and movement across severalranges (distance) over time. For this example, a person is breathing ataround 0.3 m from the mobile device, with their torso facing the mobiledevice. In the region BB and FIG. 23B, the person gets out of bed (grossbody movement) to leave the vicinity of the sensing area (e.g., leavingthe room to go to the bathroom). A while later, in relation of theregion CC and FIG. 23C, the person returns to the vicinity of thesensing area or room, such as getting back in to bed (gross bodymovement) but slightly further away from the mobile device, now withtheir back facing the mobile device, (at around 0.5 m) and theirrespiration pattern is visible. At the simplest level when interpretingthis figure, the lack of any respiratory trace or gross movementindicates a period of absence from the room.

Such movement detection can act as an input to sleep/wake processing,such as in a processing module at 1912 of FIG. 19, where sleep statesmay be estimated and/or wake or sleep designators may be generated. Alarge movement is also more likely to be a pre-cursor to a change inrange bin selection (e.g., the user has just changed position). On theother hand, breathing may diminish or cease for periods of time in thecase of SDB events detected from the respiratory parameter such asapneas or hypopneas, that can be recognized.

An alternative approach to processing the FMCW chirp signal may beapplied if the velocity signal is required (which may depend on the enduse-case). Using the alternate approach, the received signal may arriveas a delayed chirp, and an FFT operation may be performed. The signalmay then be multiplied by its conjugate to cancel the signal and keepits phase shift. The best fitting straight line to the slope may then bedetermined using multiple linear regression.

Graphically, this method gives rise to an FMCW slope, when plotting thephase angle in radians against frequency. The slope finding operationneeds to be robust to outliers. For a 10 ms FMCW chirp of 18 kHz to 20kHz sequence, the effective range is 1.8 m, with a distance resolutionof 7 mm. Overlapping FFT operations are used to estimate points on therecovered baseband signal.

In yet another approach to processing the FMCW signal directly as aone-dimensional signal, a comb filter may be applied to the synchronizedsignal (e.g., a block of 4 chirps). This is to remove the direct pathfrom Tx to Rx (direct speaker to mic component) and static reflections(clutter). An FFT may then be carried out on the filtered signal,followed by a windowing operation. A second FFT may then be carried out,followed by maximal ratio combination.

The purpose of this processing is to detect the oscillations at the sidelobes, and it outputs velocity rather than displacement. One advantageis that it estimates a 1D signal directly (without a complex binselection step) and could optionally be used to estimate a likely rangebin for the case of a single motion source (e.g., one person) in thefield of the sensor.

It can also be seen that such FMCW algorithm processing techniques mayalso be applied to 2D complex matrices such as outputted by RADARsensors utilizing FMCW chirps of various types (e.g., sawtooth ramp,triangle etc.).

5.1.3 Additional System Considerations—Speaker and/or Microphone

According to some aspects of the disclosure, the speaker and microphonemay be located on the same device (e.g., on a smartphone, tablet, laptopetc.) or on different devices with a common or otherwise synchronizedclock signal. Also, a synchronized clock signal may not be required ifthe two or more components can communicate synchronizing informationover an audio channel, or via another means such as the Internet. Insome solutions, the transmitter and receiver may use the same clock andthen no special synchronization techniques are required. Alternativemethods of realizing synchronization include utilizing a Costas loop orPLL (phase locked loop)—i.e., an approach that can “lock in to” thetransmitted signal—such as a carrier signal.

It can be important to account for any buffering in the audio pathway,to understand the cumulative impact on synchronization of transmittedand received samples, in order to minimize potential unwanted lowfrequency artefact components. In some cases, it may be desirable toplace the microphone and/or speaker near or within the bedclothes—e.g.,by using a phone headset/mic plug in device (such as used to makehands-free calls). An example is the device usually bundled with Appleand Android phones. During a calibration/setup process, the systemshould be able to select the appropriate mic (if more than one mic ispresent on a phone), speaker, and amplitude and frequency settings tosuit the system/environmental setup.

5.1.3.1.1 Calibration/Adaption of System to Component Variation and tothe Environment

It is desirable to implement a technique to optimally adapt the systemparameters to the specific phone (or other mobile device) in use, theenvironment (e.g., bedroom) and the user(s) of the system. This impliesthat the system learns over time, as the apparatus may be portable(e.g., moved to another living space, bedroom, hotel, hospital, carehome etc.), adapt to one or more living subjects in the sensing field,and to be compatible with a wide range of devices. This impliesequalization of the audio channel.

The system can auto-calibrate to the channel conditions by learning (orindeed be pre-programmed with default) device or model specificcharacteristics, and channel characteristics. Device and model specificcharacteristics include the baseline noise characteristics of thespeaker and microphone, and the ability of the mechanical components tovibrate stably at the intended frequency or frequencies, as well as theamplitude response (i.e., actual emitted volume for a target waveform,and the signal response of the receiving microphone(s)). For example, inthe case of an FMCW chirp, the magnitude of the received direct pathchirp may be used to estimate the sensitivity of the system, and if thesignal amplitude and/or phone volume needs to be automatically adjustedto achieve a desired system sensitivity (which can be related theperformance of the speaker and mic combination, including the soundpressure level (SPL) emitted by the speaker/transducer at a given phonevolume). This allows the system to support many varieties of smartdevice, such as the large and disparate ecosystem of Android phones.This can also be used to check if the user has adjusted the phone mainvolume, and if this needs to be re-adjusted automatically, or the systemneeds to adjust to the new operating conditions. Different operatingsystem (OS) revisions may also give rise to different characteristics,including audio buffer lengths, audio path latency, and likelihood ofoccasional dropouts/jitter in the Tx and/or Rx stream(s).

Key aspects of the system are captured, including ADC and DACquantization level (available bits), signal to noise ratio, simultaneousor synchronized clocking of TX and RX, and temperature and humidity ofthe room (if available). For example, it may be detected that a devicehas an optimal sampling rate of 48 kHz and perform a sub-optimalresampling if supplied with samples at 44.1 kHz; in this case, thepreferred sampling rate would be set as 48 kHz. An estimate is formed ofthe dynamic range of the system, and an appropriate signal compositionis selected.

Other characteristics include the separation (angular and distance)between the transmit and receiving components (e.g., distance betweentransmitter and receiver), and the configuration/parameters of anyactive automatic gain control (AGC) and/or active echo cancellation. Thedevice (especially a phone that uses multiple active mics) may implementsignal processing measures that are confounded by the continualtransmission of a signal, leading to unwanted oscillations in thereceived signal which need to be corrected (or indeed to adjust theconfiguration of the device where possible in order to disable theunwanted AGC or echo cancellation).

The system can be preprogrammed with the reflection coefficients ofvarious materials versus frequency (e.g., the reflection coefficient at18 kHz). If we consider the echo cancellation case in more detail, forCW (single tone), the signal is there continuously; thus, unless perfectacoustic isolation (unlikely on a smartphone), the TX signal is muchstronger than RX, and the system may be negatively impacted by theinbuilt echo canceller in phone. A CW system may experience strongamplitude modulation due to the activity of an AGC system or basic echocanceller. As an example for a CW system on a specific handset (SamsungS4 running OS “Lollipop”), the raw signal can contain an amplitudemodulation (AM) of returned signal. One strategy to address this issueis to perform very high frequency AGC on the raw samples to smooth outthe AM components that are not related to the respiratory motion.

A different type of signal such as an FMCW “chirp” may defeat an echocanceller implemented in a device in an agreeable way; indeed, an(A)FHRG or UWB approach may also be robust to an acoustic echo cancellerdirected towards voice. In the FMCW case, a chirp is short term nonstationary signal that can allow access to momentary information aboutthe room and movement in the room at a point in time and moves on; theecho canceller chases this with a lag but it is still possible to seethe returned signal. However, this behavior is related to the exactimplementation of a third party echo canceller; generally speaking, itis desirable (where possible) to disable any software or hardware (e.g.,in the CODEC) echo cancellation for the duration of the physiologicalsensing Tx/Rx usage.

Another approach is to use a continuous broadband UWB (ultra wide band)signal. Such a UWB approach is highly resilient in the case where thereis not a good response at a particular frequency. A wideband signalcould be constrained to an inaudible band, or spread within an audibleband as a hiss; such a signal can be at a low amplitude that does notdisturb a person or animal, and optionally be further “shaped” by awindow to sound pleasing to the ear.

One method to create an inaudible UWB sequence is to take an audibleprobing sequence such as a maximum length sequence (MLS—a type ofpseudorandom binary sequence) and modulate up an inaudible band. It canalso be kept at audible frequency, e.g., in the case where it can bemasked by an existing sound such as music etc. Due to the fact that themaximum lengths sequence needs to be repeated, the resulting sound isnot pure spectrally flat white noise; in fact, it can produce a soundsimilar to that produced by a commercial sound machine—whilst slowingrespiration rate detection of one or more persons. A pulse can either benarrow in time or narrow in autocorrelation function. Such “magic”sequences like MLS are periodic in both time and frequency; MLSspecifically has an excellent autocorrelation property. By estimatingthe impulse response of the room, it is possible to range gate. It isnecessary to pick out sub sample movement in order to recover abreathing signal of a subject in the room. This can be done using agroup delay extraction method; one example is to use center of gravity(first moment) as a proxy for a filtered impulse response (i.e., thegroup delay of a segment corresponds to the center of gravity of theimpulse response).

A channel model can be created automatically (e.g., on first use of an“app” on a smartdevice or piece of hardware) or be manually cued(initiated) by a user, and periodically or continuously monitor thechannel conditions, and adapt as appropriate.

A correlation or adaptive filter (like an echo canceller) can be used tointerrogate the room. Helpfully, the bit that cannot be cancelled isprimarily due to movement in room. After estimating room parameters, aneigenfilter (derived by optimizing the objective function) may be usedto transform the raw signal, which is then transformed by the impulseresponse of the room. For the case of a pseudo white noise, it ispossible to shape the target masking signal to the frequencycharacteristics of the environmental noise and improve the sleepingexperience whilst also logging biomotion data. For example, an automaticfrequency evaluation of an environment may reveal undesirable standingwaves at a particularly frequency, and adjust the transmitted frequencyto avoid such standing waves (i.e., resonant frequencies—points ofmaximum and minimum movement in the air medium/pressure nodes andanti-nodes).

In contrast, flutter echo can affect sounds above 500 Hz, withsignificant reflections from parallel walls with hard surfaces, drywalland glass and so forth. Therefore, active noise cancelling can beapplied to reduce or cancel unwanted reflections/effects seen in theenvironment. In terms of orientation of the device, the system setupwill alert the user to the optimum position of the phone (i.e., tomaximize SNR). This may require the loudspeaker of the phone to bepointed towards the chest within a particular distance range.Calibration can also detect and correct for the presence of manufactureror third party phone covers that may alter the acoustic characteristicsof the system. If the mic on the phone appears to be compromised, theuser may be asked to clean the mic opening (e.g., a phone may pick uplint, dust or other material in the mic opening that can be cleaned).

According to some aspects of the disclosure, a continuous wave (CW)approach may be applied. Unlike a range gated system using time offlight, such as FMCW, UWB, or A(FHRG), CW uses a single continuoussinusoidal tone. Unmodulated CW can use the Doppler effect when objectsare moving (i.e., return frequencies are shifted away from thetransmitted frequency), but may be unable to evaluate distance. CW maybe used, for example, for the case of a single person in bed with noother motion sources nearby, and could give a high signal to noise (SNR)in such a case. The demodulation scheme as outlined in FHRG can be usedfor the special case of a single tone (no frames per se) in order torecover a baseband signal.

Another approach is Adaptive CW. This is not specifically range gated(although can in effect have a limited range such as to detect thenearest person in a bed due to the fact that it is limited by Tx power,and room reverberation), and can make use of room modes. Adaptive CWmaintains the use of a continuous transmitted audio tone in an inaudiblerange, but within the capabilities of the transmit/receive apparatus. Byscanning across inaudible frequencies in steps, an algorithm iterativelysearches frequencies for the best available breathing signal—both infrequency content, and also time domain morphology (breathing shape). Aspacing in Tx signal of only 10 Hz may lead to quite a different shapeof demodulated respiratory waveform, with the cleared morphology beingmost appropriate for apnea (central and obstructive) and hypopneaanalysis.

Holography is concerned with wavefront reconstruction. Highly stableaudio oscillators are required for holography, where the coherent sourceis the loudspeaker, and makes use of the fact that a room has storedenergy due to reverberation (i.e., a CW signal is chosen to specificallyhave strong standing waves, as opposed to other approaches discussedpreviously that try to move out of modes, or indeed not to createstanding waves at all using frequency hopping, and/or adaptive frequencyselection).

For the case of a system with two or more loudspeakers, it becomespossible to adjust or steer the “beam” in a particular direction (e.g.,to optimally detect a single person in a bed).

When we further consider FIG. 7, if data are stored after the high passfilter 702 (e.g., a HPF with 3 dB point at 17 kHz) for later offlineanalysis (versus say an online processing system that reads and discardsraw audio data), the high pass filtering can act as a privacy filter, asthe blocked (removed/filtered out) data in the stopband contain theprimary speech information.

5.1.3.1.2 Adapting to Multiple Cooperating or Non-Cooperating Devicesand to Interferers

A system may comprise multiple microphones, or be required to cooperatewith a system proximate system (e.g., two phones running an app placedat either side of a double bed to monitor two people independently). Inother words, management of multiple transceivers is required in anenvironment—using the channel or another means such as a wireless signalor data transmitted via the internet to allow coexistence. Specifically,this means that waveforms may be adapted with the selected band in orderto minimize interference, whether it be to adjust coding sequences or inthe simplest case of a single sine wave, to detect a sine wave atapproximately 18 kHz and choose to transmit at approximately 19 kHz.Thus, the device may include a setup mode which may be activated onstartup to check the sound signals of the environment or vicinity of thedevice with signal analysis of sound of the vicinity (e.g., frequencyanalysis of sound received by microphone) and in response to theanalysis, choose a different frequency range for operation such as anon-overlapping set of frequencies from the received sound frequencies.In this way, multiple devices may operate with the sound generation andmodulation technology described herein in a common vicinity where eachdevice generates sound signals in a different frequency range. In somecases, the different frequency ranges may still be within the lowultrasonic frequency ranges described herein.

It is less likely that FMCW would be run on more than one device inproximity at a time (vs. say CW) as FMCW on a single device can detectmultiple persons; however, if more than one FMCW transmission is runningin proximity in order to maximize the available SNR, the bands may beautomatically (or through user intervention) adapted to benon-overlapping in frequency (e.g., 18-19 kHz, and 19.1-20.1 kHz etc.)or in time (where the chirps occupy the same frequency band as eachother, but have non-overlapping quiet periods, with a guard band toallow the other device's reflections to dissipate).

When using (A)FHRG with tone pairs, it can be seen that these can befrequency and/or time dithered. Frequency dithering implies a varyingfrequency shift between frames, and time dithering means that the timeof flight (based on changing the pulse duration) is changed. Such anapproach of one or both dithering methods may include reducing theprobability of room modes being generated and/or allowing two SONARsystems to coexist within “hearing” distance of each other.

One can also see that an FHRG system can be released with a pseudorandom sequence defining the tones/frames—assuming that the transitionsare such that audible harmonics are not introduced into the resultingtransmitted signal (or that appropriate comb filtering is applied toremove/attenuate the unwanted sub harmonics).

Where the TX and RX signal are generated on different hardware, a commonclock may not be available; therefore; cooperation is required. Wheretwo or more devices are in “hearing” distance of each other, optimallyemploy cooperative signal selection to avoid interference. This canallow the adaption of transmitted signals to provide best return frombed clothes.

5.1.3.1.3 Adapting to User Preferences

A simple audio sweep test can allow a user to select the lowestfrequency that they can no longer hear (e.g., 17.56 kHz, 19.3 KHz, 21.2kHz etc. etc.); this (with a small guard-band offset) can be used as thestart frequency for a generated signal. A pet set up process can also beincluded to check if a dog, cat, pet mouse or similar reacts to a samplesounds; if they do, it may be preferable to use a low amplitude audible(to humans) sound signal with modulated information. A “pet set up mode”can be implemented to check if (say) a dog responds to a particularsound, and they user can record this fact such that the system checks adifferent sound in order to find a signal that does not causediscomfort. Similarly, where discomfort to the user is noted, the systemcan configure to an alternate signal type/frequency band. White noisefeature including active signal TX can be useful where pets/children donot tolerate a specific waveform and/or where a calming masking noise(“white noise”/hiss) is desired. Thus, the mode may cycle through one ormore test sound signals and prompt a user for input regarding whetherthe tested sound signal (which may be inaudible to humans) isproblematic or not, and select a frequency for use based on the input.

5.1.3.1.4 Pausing Tx Playback Automatically when a User Interacts with aDevice

As the system may be playing back a high amplitude inaudible signal, itis desirable that this be muted (Tx paused) in a mobile device such as asmartdevice if the user is interacting with the device. Specifically,the device must be muted if brought up beside the user's ear (e.g., tomake or receive a call). Of course, once the signal is unmuted, thesystem is required to resynchronize. The system may also cause a batterydrain, so the following approach may be used. If the system is running,it should preferentially run only when the device is being charged;thus, the system may be designed to pause (or not start) if asmartdevice is not connected to a wired or wireless charger. In terms ofpausing Tx (and processing) when the device is in use, inputs can betaken from one or more of user interaction with the phone—pressing abutton, touching a screen, moving the phone (detected via an inbuiltaccelerometer if present, and/or a gyroscope, and/or an infra-redproximity sensor), or a change in location as detected with GPS orassisted GPS, or an incoming call. In the case of a notification (e.g.,a text or other message), where the device is not in “silent” mode, thesystem may temporarily pause Tx in anticipation of the user picking upthe device for a period of time.

On start-up, the system may wait a period of time before activating theTx, in order to check that the phone has been placed down on a surface,and the user is no longer interacting with it. If a range estimationapproach such as FMCW is being employed, the system may reduce the Txpower level or pause (mute) Tx if a breathing movement is detected veryclose to the device, on the basis that the minimum possible acousticoutput power should be used to meet a desired SNR level. Additionally,the recovered gesture from the demodulated baseband signal as the userreaches out for the device can be used to proactively smoothly reducethe Tx volume, before either silencing if the user actually interactswith the device, or smoothly increasing the volume to the operatinglevel if the user withdraws their hand from the vicinity of the device.

5.1.3.1.5 Data Fusion

For a system that collects (receives) an audio signal with an activetransmitted component, it is also desirable to process the full bandsignal in order to reject or utilize other patterns. For example, thiscan be used to detect speech, background noise, or other patterns thatcould swamp the TX signal. For respiratory analysis, it is highlydesirable to carry out data fusion with extracted audio waveformcharacteristics of breathing; a direct detection of the breathing soundcan be combined with the demodulated signal. Also, aspects of riskysleep or respiratory conditions with an audio component such ascoughing, wheezing, snoring etc. can be extracted—especially forapplications within the bedroom (usually a quiet environment). Breathingsounds can be from the mouth or nose, and including snoring, wheezing,gasping, whistling and so forth.

The full audio signal can also be used to estimate movement (such as agross motion like a user rolling over in bed), and distinguish fromother background non-physiologically generated noises. The SONARestimated movement (from the processed baseband signal) and the fullband passive audio signal analysis can be combined, typically with theSONAR movement (and estimated activity from that movement) takingprecedence due to the advantage of range gating (range detection) inFMWC, (A)FHRG ToF etc. over a non-range specific passive acousticanalysis. The duration and intensity of detected noise can also offerinsight into unwanted de-synchronizations—such as when a fan is placedin close proximity, or a heating or cooling system is excessively noisy.Acoustic fan noise may also be seen as increased 1/f signal in the SONARbaseband signal analysis. Where actual user sounds can be detected overthe noise floor, but the SONAR Rx signal is of very poor quality, thesystem can fall back to a processing mode where physiological sounds aremapped to activity, and directly drive a reduced accuracy sleep/wakedetector, based purely on these activity indices. The system may alsofeedback to the user on optimal fan placement, and/or adapt itsfrequencies of operation to bands where the interference is reduced.Other noise sources such as fluorescent tubes/bulbs and LED ballasts canbe detected, and the system adapted to a more preferentially SONARfrequency(ies) of operation. Thus, the device may also process audiosignals by traditional sound processing methods (in addition to thedemodulation processing techniques described herein) received via themicrophone to evaluate any one or more of environmental sounds, speechsounds and breathing sounds for detection of user motion and relatedcharacteristics.

Speech detection can also be used as a privacy function, in order toactively discard any temporary data that can contain privateinformation. Processing can be performed to reject potential confoundingfactors—ticking analog clock, TV, streaming media on tablet, fans, airconditioning units, forced heating systems, traffic/street noise and soforth. Thus, biomotion information can be extracted from the audiblespectrum, and combined with data extracted from the demodulated schemeto improve overall accuracy.

According to some aspects of the disclosure, the light sensor on adevice, such as a smartphone, may provide a separate input into thesystem, suggesting whether the person is attempting to sleep, or iswatching television, reading, using a tablet, etc. Using the temperaturesensor on the phone and or available humidity sensing (or weather databased on location) can be used to augment the channelestimation/propagating of the transmitted signal. Interaction with thephone itself can provide additional information on level of alertness ofuser and/or fatigue state.

Understanding the motion of the sensing device via an internal motionsensor, such as a MEMS accelerometer can be used to disable the acousticsensing processes when phone is in movement. Fusion of sensor data withaccelerometer data may be used to augment movement detection.

It is noted that while the systems and methods described herein aredescribed as being implemented by a mobile device, in other aspects ofthe disclosure, the systems and methods may be implemented by a fixeddevice. For example, the systems and methods described herein may beimplemented by a bedside consumer monitoring device or a medical device,such as a flow generator (e.g., a CPAP machine) for treating sleepdisordered breathing or other respiratory conditions.

5.1.3.1.6 Cardiac Information

As previously mentioned, in addition to respiration information, thesound generation and reflection analysis of the various technologyversions previously described, may be implemented for other periodicinformation as well such as cardiac information detection or heart rate,from the produced motion-related signal. In the example of FIG. 21,cardiac decision processing may be an optionally added module. Aballistocardiogram based cardiac peak detection can be applied duringthe FFT based 2D processing stage (just as respiration detection isperformed) but looking at a higher frequency band.

Alternatively, wavelets (as an alternative or addition to FFTs)—such asa discrete wavelet transform processing each of the I then Q signals—orsimultaneously processing using a discrete complex wavelet transform canbe used for the 2D processing stage to split out gross motion,respiration, and cardiac signals.

Time-frequency processing such as wavelet based methods (e.g.,discretized continuous wavelet transform—DCWT—using for exampleDaubechies Wavelets) can perform both detrending, as well as direct bodymotion, respiration, and cardiac signal extraction. The cardiac activityis reflected in signals at higher frequencies, and this activity can beaccessed by filtering with a bandpass filter with a pass band of a rangefrom 0.7 to 4 Hz (48 beats per minute to 240 beats per minute). Activitydue to gross motion is typically in the range 4 Hz to 10 Hz. It shouldbe noted that there can be overlap in these ranges. Strong (clean)breathing traces can give rise to strong harmonics, and these need to betracked in order to avoid confusing For example, signal analysis in someversions of the present technology may include any of the methodologiesdescribed in International Patent Application Publication No.WO2014/047310, the entire disclosure of which is incorporated herein byreference, including, for example, methods of wavelet de-noising ofrespiratory signals.

5.1.3.1.7 System Examples

In general, the technology versions of the present application may beimplemented by one or more processors configured with the monitoringrelated methodologies such as the algorithms or methods of the modulesdescribed in the more detail herein. Thus, the technology may beimplemented with integrated chips, one or more memories and/or othercontrol instruction, data or information storage medium. For example,programmed instructions encompassing any of the methodologies describedherein may be coded on integrated chips in the memory of a suitabledevice. Such instructions may also or alternatively be loaded assoftware or firmware using an appropriate data storage medium. Thus, thetechnology may include a processor-readable medium, or computer-readabledata storage medium, having stored thereon processor-executableinstructions which, when executed by one or more processors, cause theprocessor to perform any of the methods or aspects of the methodsdescribed herein. In some cases, a server, or other networked computingapparatus, may include or otherwise be configured to have access to sucha processor-readable data storage medium. The server may be configuredto receive requests for downloading the processor executableinstructions of the processor-readable data storage medium to aprocessing device over a network. Thus, the technology may involve amethod of a server having access to the processor-readable data storagemedium. The server receives a request for downloading the processorexecutable instructions of the processor-readable data storage medium,such as for downloading the instructions to a processing device such asa computing device or portable computing device (e.g., smart phone) overa network. The server may then transmit the processor executableinstructions of the computer-readable data storage medium to the devicein response to the request. The device may then execute the processorexecutable instructions, such as if the processor executableinstructions are stored on another processor-readable data storagemedium of the device.

5.1.3.1.8 Other Portable or Electronic Processing Devices

As previously mentioned, the sound sensing methodologies describedherein may be implemented by one or more processors of electronicprocessing devices or computing devices such as smart phones, laptops,portable/mobile devices, mobile phones, tablet computers, etc. Thesedevices may be typically understood to be portable or mobile. However,other similar electronic processing devices may also be implemented withthe technologies described herein.

For example, many homes and vehicles contain electronic processingdevices that are capable of emitting and recording sounds such as in thelow frequency ultrasonic range at just above the human hearingthreshold—e.g., smart speakers, active sound bars, smart devices, otherdevices supporting voice and other virtual assistants. A smart speakeror similar device typically includes communication components via awired or wireless means (such as Bluetooth, Wi-Fi, Zig Bee, mesh, peerto peer networking etc.) such as for communication to and from otherhome devices such as for home automation, and/or a network such as theInternet. Unlike a standard speaker that is designed to simply emit anacoustic signal, a smart speaker usually includes one or more processorsand one or more speakers, as well as one or more microphones (mic(s)).The mic(s) may be used to interface to intelligent assistants(artificial intelligence (AI) systems) in order to provide personalizedvoice control. Some examples are Google Home, Apple HomePod, AmazonEcho, with voice activation using “OK Google”, “Hey Siri”, “Alexa”phrases. These devices may be portable, and may be typically intendedfor use at a particular location. Their connected sensors may beconsidered to be part of the Internet of Things (IoT). Other devicessuch as active sound bars (i.e., including microphones), smarttelevisions (which may typically be stationary devices), and mobilesmart devices can also be utilized.

Such devices and systems can be adapted to perform physiological sensingusing low frequency ultrasonic techniques described herein.

For devices with multiple transducers, it is possible to implement beamforming—i.e., where signal processing is employed to provide directionalor spatial selectivity of signals sent to, or received from, an array ofsensors (e.g., speakers). This is typically a “far field” problem wherethe wavefront is relatively flat for low frequency ultrasound (asopposed to medical imaging, which is “near field”). For a pure CWsystem, audio waves travel out from the speaker, leading to areas ofmaxima and minima. However, if multiple transducers are available, itbecomes possible to control this radiation pattern to our advantage—anapproach known as beam forming. On the receive side, multiplemicrophones can also be used. This allows the acoustic sensing to bepreferentially steering (e.g., steering the emitted sound and/or thereceived sound waves) in a direction, and swept across a region. For thecase of a user in bed, the sensing can be steered towards the subject—ortowards multiple subjects where there are say two persons in the bed.

By way of additional example, the technologies described herein may beimplemented in wearable devices such as non-invasive devices (e.g.,smart watches) or even invasive devices (e.g., implant chips orimplantable devices). These portable devices may also be configured withthe present technology.

5.2 Other Remarks

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in Patent Office patent files orrecords, but otherwise reserves all copyright rights whatsoever.

Unless the context clearly dictates otherwise and where a range ofvalues is provided, it is understood that each intervening value, to thetenth of the unit of the lower limit, between the upper and lower limitof that range, and any other stated or intervening value in that statedrange is encompassed within the technology. The upper and lower limitsof these intervening ranges, which may be independently included in theintervening ranges, are also encompassed within the technology, subjectto any specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the technology.

Furthermore, where a value or values are stated herein as beingimplemented as part of the present technology, it is understood thatsuch values may be approximated, unless otherwise stated, and suchvalues may be utilized to any suitable significant digit to the extentthat a practical technical implementation may permit or require it.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this technology belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present technology, a limitednumber of the exemplary methods and materials are described herein.

When a particular material is identified as being used to construct acomponent, obvious alternative materials with similar properties may beused as a substitute. Furthermore, unless specified to the contrary, anyand all components herein described are understood to be capable ofbeing manufactured and, as such, may be manufactured together orseparately.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include their plural equivalents,unless the context clearly dictates otherwise.

All publications mentioned herein are incorporated herein by referencein their entirety to disclose and describe the methods and/or materialswhich are the subject of those publications. The publications discussedherein are provided solely for their disclosure prior to the filing dateof the present application. Nothing herein is to be construed as anadmission that the present technology is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dates,which may need to be independently confirmed.

The terms “comprises” and “comprising” should be interpreted asreferring to elements, components, or steps in a non-exclusive manner,indicating that the referenced elements, components, or steps may bepresent, or utilized, or combined with other elements, components, orsteps that are not expressly referenced.

The subject headings used in the detailed description are included onlyfor the ease of reference of the reader and should not be used to limitthe subject matter found throughout the disclosure or the claims. Thesubject headings should not be used in construing the scope of theclaims or the claim limitations.

Although the technology herein has been described with reference toparticular examples, it is to be understood that these examples aremerely illustrative of the principles and applications of thetechnology. In some instances, the terminology and symbols may implyspecific details that are not required to practice the technology. Forexample, although the terms “first” and “second” may be used, unlessotherwise specified, they are not intended to indicate any order but maybe utilized to distinguish between distinct elements. Furthermore,although process steps in the methodologies may be described orillustrated in an order, such an ordering is not required. Those skilledin the art will recognize that such ordering may be modified and/oraspects thereof may be conducted concurrently or even synchronously.

It is therefore to be understood that numerous modifications may be madeto the illustrative examples and that other arrangements may be devisedwithout departing from the spirit and scope of the technology.

1. A processor-readable medium, having stored thereonprocessor-executable instructions which, when executed by a processor,cause the processor to detect physiological movement of a user, theprocessor-executable instructions comprising: instructions to controlproducing, via a speaker coupled to an electronic processing device, asound signal in a vicinity that includes a user; instructions to controlsensing, via a microphone coupled to the electronic processing device, asound signal reflected from the user; instructions to process the sensedsound signal; and instructions to detect a breathing signal from theprocessed sound signal, wherein the sound signal comprises any of: (a) atone pair forming a pulse, and (b) a repeated waveform with changingfrequencies that form a triangular or a sinusoidal waveform.
 2. Theprocessor-readable medium of claim 1 wherein the sound signal is in aninaudible sound range.
 3. The processor-readable medium of claim 1wherein the sound signal comprises the tone pair forming a pulse.
 4. Theprocessor-readable medium of claim 1 wherein the sound signal comprisesa sequence of frames, each frame comprising a series of tone pairs, eachtone pair being associated with a respective time slot within the frame.5. (canceled)
 6. The processor-readable medium of claim 1, wherein thetone pair comprises a first frequency and a second frequency, andwherein the first frequency and the second frequency are orthogonal toeach other.
 7. The processor-readable medium of claim 4 wherein theseries of tone pairs in a frame comprises a first tone pair and a secondtone pair, wherein frequencies of a first tone pair are different fromfrequencies of a second tone pair.
 8. The processor-readable medium ofclaim 4 wherein a tone pair of a time slot of the frame has a zeroamplitude at a beginning and an end of the time slot and has a rampingamplitude to and from a peak amplitude between the beginning and theend.
 9. The processor-readable medium of claim 4 wherein a time width ofthe frame varies. 10-11. (canceled)
 12. The processor-readable medium ofclaim 1 wherein a sequence of tone pairs of a frame of slots form apattern of different frequencies with respect to different slots of theframe. 13-20. (canceled)
 21. The processor-readable medium of claim 1when dependent on claim 4, wherein a duration of a respective time slotof the frame is equal to one divided by a difference between frequenciesof a tone pair.
 22. The processor-readable medium of claim 1 wherein thesound signal comprises the repeated waveform with changing frequencies.23. The processor-readable medium of claim 22 wherein the repeatedwaveform is phase-continuous. 24-25. (canceled)
 26. Theprocessor-readable medium of claim 22 further comprising instructions tovary one or more parameters of a form of the repeated waveform whereinthe one or more parameters comprises any one or more of (a) a locationof a peak in a repeated portion of the repeated waveform, (b) a slope ofa ramp of a repeated portion of the repeated waveform, and (c) afrequency range of a repeated portion of the repeated waveform.
 27. Theprocessor-readable medium of claim 22 wherein the repeated waveform withchanging frequencies comprises the triangular waveform.
 28. Theprocessor-readable medium of claim 27 wherein the triangular waveform isa symmetric triangular waveform. 29-40. (canceled)
 41. Theprocessor-readable medium of claim 1, wherein the processor-executableinstructions further comprise: instructions to calibrate sound-baseddetection of body movement by an assessment of one or morecharacteristics of the electronic processing device; and instructions togenerate the sound signal based on the assessment. 42-50. (canceled) 51.A server with access to the processor-readable medium of claim 1,wherein the server is configured to receive requests for downloading theprocessor-executable instructions of the processor-readable medium to anelectronic processing device over a network.
 52. An electronic devicecomprising: one or more processors; a speaker coupled to the one or moreprocessors; a microphone coupled to the one or more processors; and aprocessor-readable medium of claim
 1. 53. A method of a server havingaccess to the processor-readable medium of claim 1, the methodcomprising receiving, at the server, a request for downloading theprocessor-executable instructions of the processor-readable medium to anelectronic processing device over a network; and transmitting theprocessor-executable instructions to the electronic processing device inresponse to the request.
 54. A method of a processor for detecting bodymovement using an electronic device, comprising: accessing, with aprocessor, the processor-readable medium of claim 1, executing, in theprocessor, the processor-executable instructions of theprocessor-readable medium.
 55. A method of a processor for detectingbody movement using a electronic device, comprising: controllingproducing, via a speaker coupled to the electronic device, a soundsignal in a vicinity that includes a user; controlling sensing, via amicrophone coupled to the electronic device, a sound signal reflectedfrom the user; processing the sensed reflected sound signal; anddetecting a breathing signal from the processed reflected sound signal,wherein the sound signal comprises any of: (a) a tone pair forming apulse, and (b) a repeated waveform with changing frequencies that form atriangular or a sinusoidal waveform.
 56. The method of claim 55 whereinthe sound signal comprises the repeated waveform with changingfrequencies that forms the triangular.
 57. A method for detectingmovement and breathing using an electronic device, comprising:transmitting, via a speaker on the electronic device, a sound signaltowards a user; sensing, via a microphone on the electronic device, areflected sound signal, the reflected sound signal being reflected fromthe user; and detecting a breathing and motion signal from the reflectedsound signal, wherein the sound signal comprises any of: (a) a tone pairforming a pulse, and (b) a repeated waveform with changing frequenciesthat form a triangular or a sinusoidal waveform.
 58. The method of claim57, wherein the sound signal is an inaudible sound signal.
 59. Themethod of claim 57, prior to transmitting, modulating the sound signalusing one of a FMCW modulation scheme, a FHRG modulation scheme, anAFHRG modulation scheme, a CW modulation scheme, a UWB modulationscheme, or an ACW modulation scheme.
 60. The method of claim 57, whereinthe sound signal is a modulated low frequency ultrasonic sound signalcomprising a plurality of frequency pairs transmitted as a frame. 61.The method of claim 57, further comprising: upon sensing the reflectedsound signal, demodulating the reflected sound signal, whereindemodulating comprises: performing a filter operation on the reflectedsound signal; and synchronizing the filtered reflected sound signal withtiming of the transmitted sound signal.
 62. The method of claim 57,wherein the electronic device is a mobile electronic device and whereingenerating the sound signal comprises: performing a calibration functionto assess one or more characteristics of the mobile electronic device;and generating the sound signal based on the calibration function. 63.The method of claim 62, wherein the calibration function is configuredto determine at least one hardware, environment, or user specificcharacteristic.
 64. The method of claim 61, wherein the filter operationis a high pass filter operation.
 65. A method of detecting motion andbreathing, comprising: producing a sound signal directed towards a user;sensing a sound signal reflected from the user; and detecting abreathing and motion signal from the sensed reflected sound signal,wherein the sound signal comprises any of: (a) a tone pair forming apulse, and (b) a repeated waveform with changing frequencies that form atriangular or a sinusoidal waveform.
 66. The method of claim 65, whereinthe producing, transmitting, sensing, and detecting are performed at abedside device.
 67. The method of claim 66, wherein the bedside deviceis a CPAP device.
 68. The method of claim 65 wherein the sound signalcomprises the repeated waveform with changing frequencies that forms thetriangular waveform.
 69. An electronic device to detect physiologicalmovement of a user, the electronic device comprising: one or moreprocessors; a speaker coupled to the one or more processors; amicrophone coupled to the one or more processors; wherein the one ormore processors are configured to: control producing, via the speaker, asound signal in a vicinity that includes a user; control sensing, viathe microphone, a sound signal reflected from the user; process thesensed sound signal; and detect a breathing signal from the processedsound signal, wherein the sound signal comprises any of: (a) a tone pairforming a pulse, and (b) a repeated waveform with changing frequenciesthat form a triangular or a sinusoidal waveform.
 70. The electronicdevice of claim 69 wherein the sound signal is in an inaudible soundrange.
 71. The electronic device of claim 69 wherein the sound signalcomprises the tone pair forming a pulse.
 72. The electronic device ofclaim 69 wherein the sound signal comprises a sequence of frames, eachframe comprising a series of tone pairs, each tone pair being associatedwith a respective time slot within the frame.
 73. The electronic deviceof claim 69 wherein a tone pair comprises a first frequency and a secondfrequency, wherein the first frequency and second frequency aredifferent.
 74. The electronic device of claim 73 wherein the firstfrequency and second frequency are orthogonal to each other.
 75. Theelectronic device of claim 72 wherein the series of tone pairs in aframe comprises a first tone pair and a second tone pair, whereinfrequencies of a first tone pair are different from frequencies of asecond tone pair.
 76. The electronic device of claim 72 wherein a tonepair of a time slot of the frame has a zero amplitude at a beginning andan end of the time slot and has a ramping amplitude to and from a peakamplitude between the beginning and the end.
 77. The electronic deviceof claim 72 wherein a time width of the frame varies.
 78. The electronicdevice of claim 69 wherein a sequence of tone pairs of a frame of slotsform a pattern of different frequencies with respect to different slotsof the frame.